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1
Schreiber, Jonah, and Philip Bramstång. "Evolutionary Tuning of Chess Playing Software." Thesis, KTH, Fysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-126387.
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In the ambition to create intelligent computer players, the game of chess is probably the most well-studied game. Much work has already been done on producing good methods to search a chess game tree and to statically evaluate chess positions. However, there is little consensus on how to tune the parameters of a chess program’s search and evaluation functions. What set of parameters makes the program play its strongest? This paper attempts to answer this question by observing the results of tuning a custom chess-playing implementation, called Agent, using genetic algorithms and evolutionary programming. We show not only how such algorithms improve the program’s playing strength overall, but we also compare the improved program’s strength to other versions of Agent.
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Bronson, Darren (Darren Frazier) 1970. "Best practices for evolutionary software development." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80490.
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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 41-42).
by Darren Bronson.
S.M.
M.B.A.
3
Davis, Edward V. Jr. "Software testing for evolutionary iterative rapid prototyping." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/27570.
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Approved for public release; distribution unlimited.Rapid prototyping is emerging as a promising software development paradigm. It provides a systematic and automatable means of developing a software system under circumstances where initial requirements are not well known or where requirements change frequently during development. To provide high software quality assurance requires sufficient software testing. The unique nature of evolutionary iterative prototyping is not well-suited for classical testing methodologies, therefore the need exists for a testing methodology tailored for this prototyping paradigm. This thesis surveys current prototyping and testing practices to provide a foundation for developing a software testing methodology for prototyping. The thesis then describes a testing methodology for rapid prototyping, Spiral Testing, and the Test Goal Tracking System (TGTS), a requirements-based testing tool developed for use with the Computer Aided Prototyping System (CAPS). TGTS provides the first in an anticipated family of testing tools to support the CAPS environment. This thesis shows key prototyping characteristics impinging on testing, the value of Spiral Testing and the feasibility and qualities of complementary testing tools to support evolutionary iterative rapid prototyping.
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Gross, Hans-Gerhard. "Measuring evolutionary testability of real-time software." Thesis, University of South Wales, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365087.
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Davis, Edward V. "Software testing for evolutionary iterative rapid prototyping." Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA232555.
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Thesis (M.S. in Computer Science)--Naval Postgraduate School, December 1990.Thesis Advisor(s): Shimeall, Timothy J. Second Reader: Barnes, Patrick D. "December 1990." Description based on title screen as viewed on March 29, 2010. DTIC Identifier(s): Computer Program Verification, Prototypes, Software Engineering, User Manuals. Author(s) subject terms: Software Testing, Software Prototyping, Rapid Prototyping, Reusable Components, Requirements-based Testing, Software Testing Tools. Includes bibliographical references (p. 276-281). Also available in print.
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Wilkerson, Joshua Lee. "Co-evolutionary automated software correction: a proof of concept." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2008. http://scholarsmine.mst.edu/thesis/pdf/Wilkerson_09007dcc80642bb4.pdf.
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Thesis (M.S.)--Missouri University of Science and Technology, 2008.Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 18, 2009) Includes bibliographical references (p. 62-64).
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Forsyth, Henry Lee. "Evolutionary environmental modelling in self-managing software systems." Thesis, Liverpool John Moores University, 2010. http://researchonline.ljmu.ac.uk/6009/.
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Over recent years, the increasing richness and sophistication of modem software systems has challenged conventional design-time software modelling analysis and has led to a number of studies exploring non-conventional approaches particularly those inspired by nature. The natural world routinely produces organisms that can not only survive but also flourish in changing environments as a consequence of their ability to adapt and therefore improve their fitness in relation to the external environments in which they exist. Following this biologically inspired systems' design approach, this study aims to test the hypothesis - can evolutionary techniques for runtime modelling of a given system's environment be more effective than traditional approaches, which are increasingly difficult to specify and model at design-time? This work specifically focuses on investigating the requirements for software environment modelling at runtime via a proposed systemic integration of Learning Classifier Systems and Genetic Algorithms with the well-known managerial cybernetics Viable Systems Model. The main novel contribution of this thesis is that it provides an evaluation of an approach by which software can create and crucially, maintain a current model of the environment, allowing the system to react more effectively to changes in that environment, thereby improving robustness and performance of the system. Detailed novel contributions include an evaluation of a variety of environmental modelling approaches to improving system robustness, the use of Learning Classifier Systems and genetic algorithms to provide the modelling element required of effective adaptive software systems. It also provides a conceptual framework of an Environmental Modelling, Monitoring and Adaptive system (EMMA) to manage the various elements required to achieve an effective environmental control system. The key result of this research has been to demonstrate the value of the guiding principles provided by the field of cybernetics and the potential of Beer's 2 cybernetically based Viable System Model in providing a learning framework, and subsequently a roadmap, to developing self-managing autonomic systems. The work is presented using a virtual world platform called "Second Life". This platform was used for experimental design and testing of results.
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Chatterjee, Raja. "KBMS-based evolutionary prototyping of object-oriented software systems /." CLICK ON LINK, THEN SEARCH BY AUTHOR OR TITLE; ACCESS RESTRICTED TO COMPUTERS ON UF CAMPUS OR WITH UF IP ADDRESSES, 1998. http://wwwlib.umi.com/cr/ufl/main.
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Thesis (Ph. D.)--University of Florida, 1998.Typescript. Vita. Includes bibliographical references (leaves 115-118). Full text also available from UMI Current Research @ database; Adobe Acrobat Reader required to display text; see LINKS to connect
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Simons, Christopher Lloyd. "Interactive evolutionary computing in early lifecycle software engineering design." Thesis, University of the West of England, Bristol, 2011. http://eprints.uwe.ac.uk/15599/.
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Design is fundamental to software development. Indeed, early lifecycle software engineering design is crucial and has significant impact of subsequent development activities. Inferior designs can result in deleterious down-stream consequences. Therefore improving the traceability, structural integrity and elegance of software design has significant potential for enhancing software development productivity. However, early lifecycle software design is a demanding and non-trivial task for software engineers to perform and current computational tool support for software engineers is limited. Thus to address this limitation, this thesis investigates the potential of interactive evolutionary search and complementary computational intelligence to enable the exploration and discovery of useful and interesting software designs relating to the design problem at hand. To enable evolutionary search and exploration of possible design solutions, a novel, discrete, object-based representation of both design problem and design solution is proposed. Associated genetic operators including self-adapting mutation are also proposed. Experiments show that this novel representation enables highly effective search and exploration of the software design solution space. Next, software agents are introduced to facilitate an interactive framework for natural collaborative designer / computer interaction. Empirical investigations reveal that colourful visualisation of software designs engages the designer. Furthermore, with enhanced generation of multiple candidate designs, opportunities for periods of designer reflection are presented thus enabling sudden design discovery. Design elegance is an important but complex factor in software design. Four novel quantitative elegance measures are proposed which enhance the interactive design experience by selecting elegant software designs for designer evaluation. Using designer elegance evaluation as reward, reward-based machine learning is exploited to steer a dynamic, multi-objective search according to designer elegance intentions. Designer interactivity is further enhanced by a dynamic, fitness-proportionate interactive interval, which judiciously varies the number of evolutionary generations between interactions to promote search and exploration and further reduce use fatigue. The integration of interactive, dynamic evolutionary search with software agents and reward-based learning is found to produce an engaging, compelling interactive experience for software designers, successfully enabling the search, exploration and discovery of fruitful, interesting and useful early lifecycle software designs.
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Harkin, James. "Hardware software partitioning : a reconfigurable and evolutionary computing approach." Thesis, University of Ulster, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274414.
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Hönel, Sebastian. "Efficient Automatic Change Detection in Software Maintenance and Evolutionary Processes." Licentiate thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-94733.
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Software maintenance is such an integral part of its evolutionary process that it consumes much of the total resources available. Some estimate the costs of maintenance to be up to 100 times the amount of developing a software. A software not maintained builds up technical debt, and not paying off that debt timely will eventually outweigh the value of the software, if no countermeasures are undertaken. A software must adapt to changes in its environment, or to new and changed requirements. It must further receive corrections for emerging faults and vulnerabilities. Constant maintenance can prepare a software for the accommodation of future changes. While there may be plenty of rationale for future changes, the reasons behind historical changes may not be accessible longer. Understanding change in software evolution provides valuable insights into, e.g., the quality of a project, or aspects of the underlying development process. These are worth exploiting, for, e.g., fault prediction, managing the composition of the development team, or for effort estimation models. The size of software is a metric often used in such models, yet it is not well-defined. In this thesis, we seek to establish a robust, versatile and computationally cheap metric, that quantifies the size of changes made during maintenance. We operationalize this new metric and exploit it for automated and efficient commit classification. Our results show that the density of a commit, that is, the ratio between its net- and gross-size, is a metric that can replace other, more expensive metrics in existing classification models. Models using this metric represent the current state of the art in automatic commit classification. The density provides a more fine-grained and detailed insight into the types of maintenance activities in a software project. Additional properties of commits, such as their relation or intermediate sojourn-times, have not been previously exploited for improved classification of changes. We reason about the potential of these, and suggest and implement dependent mixture- and Bayesian models that exploit joint conditional densities, models that each have their own trade-offs with regard to computational cost and complexity, and prediction accuracy. Such models can outperform well-established classifiers, such as Gradient Boosting Machines. All of our empirical evaluation comprise large datasets, software and experiments, all of which we have published alongside the results as open-access. We have reused, extended and created datasets, and released software packages for change detection and Bayesian models used for all of the studies conducted.
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Johnson, Colin G. "A design framework for evolutionary algorithms." Thesis, University of Kent, 2003. https://kar.kent.ac.uk/13944/.
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Alali, Abdulkareem. "Improved Methods for Mining Software Repositories to Detect Evolutionary Couplings." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1406565384.
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Makai, Matthew Charles. "Incorporating Design Knowledge into Genetic Algorithm-based White-Box Software Test Case Generators." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32029.
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This thesis shows how to incorporate Unified Modeling Language sequence
diagrams into genetic algorithm-based automated test case generators to increase the code
coverage of their resulting test cases. Automated generation of test data through
evolutionary testing was proven feasible in prior research studies. In those previous
investigations, the metrics used for determining the test generation method effectiveness
were the percentages of testing statement and branch code coverage achieved. However,
the code coverage realized in those preceding studies often converged at suboptimal
percentages due to a lack of guidance in conditional statements. This study compares the
coverage percentages of 16 different Java programs when test cases are automatically
generated with and without incorporating associated UML sequence diagrams. It
introduces a tool known as the Evolutionary Test Case Generator, or ETCG, an automatic
test case generator based on genetic algorithms that provides the ability to incorporate
sequence diagrams to direct the heuristic search process and facilitate evolutionary
testing. When the generator uses sequence diagrams, the resulting test cases showed an
average improvement of 21% in branch coverage and 8% in statement coverage over test
cases produced without using sequence diagrams.Master of Science
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Lüders, Frank. "An evolutionary approach to software components in embedded real-time systems /." Västerås : Department of Computer Science and Electronics, Mälardalen University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-166.
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Lycett, Mark Geoffrey. "The development of component-based evolutionary information systems." Thesis, Brunel University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266634.
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Nguyen, Trung Thanh. "Continuous dynamic optimisation using evolutionary algorithms." Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/1296/.
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Evolutionary dynamic optimisation (EDO), or the study of applying evolutionary algorithms to dynamic optimisation problems (DOPs) is the focus of this thesis. Based on two comprehensive literature reviews on existing academic EDO research and real-world DOPs, this thesis for the first time identifies some important gaps in current academic research where some common types of problems and problem characteristics have not been covered. In an attempt to close some of these gaps, the thesis makes the following contributions: First, the thesis helps to characterise DOPs better by providing a new definition framework, two new sets of benchmark problems (for certain classes of continuous DOPs) and several new sets of performance measures (for certain classes of continuous DOPs). Second, the thesis studies continuous dynamic constrained optimisation problems (DCOPs), an important and common class of DOPs that have not been studied in EDO research. Contributions include developing novel optimisation approaches (with superior results to existing methods), analysing representative characteristics of DCOPs, identifying the strengths/weaknesses of existing methods and suggesting requirements for an algorithm to solve DCOPs effectively. Third, the thesis studies dynamic time-linkage optimisation problems (DTPs), another important and common class of DOPs that have not been well-studied in EDO research. Contributions include developing a new optimisation approach (with better results than existing methods in certain classes of DTPs), analysing the characteristics of DTPs and the strengths and weaknesses of existing EDO methods in solving certain classes of DTPs.
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Yang, Xile. "Automatic software test data generation from Z specifications using evolutionary algorithms." Thesis, University of South Wales, 1998. https://pure.southwales.ac.uk/en/studentthesis/automatic-software-test-data-generation-from-z-specifications-using-evolutionary-algorithms(fd661850-9e09-4d28-a857-d551612ccc09).html.
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Test data sets have been automatically generated for both numerical and string data types to test the functionality of simple procedures and a good sized UNIX filing system from their Z specifications. Different structured properties of software systems are covered, such as arithmetic expressions, existential and universal quantifiers, set comprehension, union, intersection and difference, etc. A CASE tool ZTEST has been implemented to automatically generate test data sets. Test cases can be derived from the functionality of the Z specifications automatically. The test data sets generated from the test cases check the behaviour of the software systems for both valid and invalid inputs. Test cases are generated for the four boundary values and an intermediate value of the input search domain. For integer input variables, high quality test data sets can be generated on the search domain boundary and on each side of the boundary for both valid and invalid tests. Adaptive methods such as Genetic Algorithms and Simulated Annealing are used to generate test data sets from the test cases. GA is chosen as the default test data generator of ZTEST. Direct assignment is used if it is possible to make ZTEST system more efficient. Z is a formal language that can be used to precisely describe the functionality of computer systems. Therefore, the test data generation method can be used widely for test data generation of software systems. It will be very useful to the systems developed from Z specifications.
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Mambrini, Andrea. "Theory grounded design of genetic programming and parallel evolutionary algorithms." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5928/.
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Evolutionary algorithms (EAs) have been successfully applied to many problems and applications. Their success comes from being general purpose, which means that the same EA can be used to solve different problems. Despite that, many factors can affect the behaviour and the performance of an EA and it has been proven that there isn't a particular EA which can solve efficiently any problem. This opens to the issue of understanding how different design choices can affect the performance of an EA and how to efficiently design and tune one. This thesis has two main objectives. On the one hand we will advance the theoretical understanding of evolutionary algorithms, particularly focusing on Genetic Programming and Parallel Evolutionary algorithms. We will do that trying to understand how different design choices affect the performance of the algorithms and providing rigorously proven bounds of the running time for different designs. This novel knowledge, built upon previous work on the theoretical foundation of EAs, will then help for the second objective of the thesis, which is to provide theory grounded design for Parallel Evolutionary Algorithms and Genetic Programming. This will consist in being inspired by the analysis of the algorithms to produce provably good algorithm designs.
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Karunarathne, Lalith. "Network coding via evolutionary algorithms." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/57047/.
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Network coding (NC) is a relatively recent novel technique that generalises network operation beyond traditional store-and-forward routing, allowing intermediate nodes to combine independent data streams linearly. The rapid integration of bandwidth-hungry applications such as video conferencing and HDTV means that NC is a decisive future network technology. NC is gaining popularity since it offers significant benefits, such as throughput gain, robustness, adaptability and resilience. However, it does this at a potential complexity cost in terms of both operational complexity and set-up complexity. This is particularly true of network code construction. Most NC problems related to these complexities are classified as non deterministic polynomial hard (NP-hard) and an evolutionary approach is essential to solve them in polynomial time. This research concentrates on the multicast scenario, particularly: (a) network code construction with optimum network and coding resources; (b) optimising network coding resources; (c) optimising network security with a cost criterion (to combat the unintentionally introduced Byzantine modification security issue). The proposed solution identifies minimal configurations for the source to deliver its multicast traffic whilst allowing intermediate nodes only to perform forwarding and coding. In the method, a preliminary process first provides unevaluated individuals to a search space that it creates using two generic algorithms (augmenting path and linear disjoint path). An initial population is then formed by randomly picking individuals in the search space. Finally, the Multi-objective Genetic algorithm (MOGA) and Vector evaluated Genetic algorithm (VEGA) approaches search the population to identify minimal configurations. Genetic operators (crossover, mutation) contribute to include optimum features (e.g. lower cost, lower coding resources) into feasible minimal configurations. A fitness assignment and individual evaluation process is performed to identify the feasible minimal configurations. Simulations performed on randomly generated acyclic networks are used to quantify the performance of MOGA and VEGA.
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May, Peter S. "Test data generation : two evolutionary approaches to mutation testing." Thesis, University of Kent, 2007. https://kar.kent.ac.uk/24023/.
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Jia, Guanbo. "Community detection in complex networks using evolutionary computation." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7483/.
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In real world many complex systems can be naturally represented as complex networks of which one distinctive feature is the community structure. The community detection, i.e., identifying the community structure, provides insight into the relationship and interaction between network function and topology and has become increasingly important in many scientific fields. In this thesis, we firstly propose a cooperative coevolutionary module identification algorithm named CoCoMi to address the scalability problem when detecting community structures in especially medium and large-scale complex networks. Secondly, we propose a consensus community detection algorithm based on the multimodal optimization and fast Surprise named CoCoMOS to detect community structures in complex networks. Thirdly, we propose an adaptive ensemble selection and multimodal optimization based consensus community detection algorithm named MASCOD to find high quality and stable consensus partitions of community structures in complex networks. The performance of these three proposed algorithms is evaluated on some well-known social, artificial and biological complex networks and experimental results demonstrate that all these three proposed algorithms have very competitive performance compared with other state-of-the-art community detection algorithms.
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Meyer, Seva. "Understanding Software Adaptation and Evolution." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-45347.
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Software maintenance is a significant part of software system lifetime. Softwaresystem lifetime incorporates many processes including software adaptation and software evolution. These processes collide into one another and create confusion as theboundaries that separate them are often difficult to distinguish. Knowing what exactly these concepts indicate and how they are related can bring simplicity to futuredevelopment of adaptive systems. The following document presents a performed systematic literature review, which aims to outline the similarities and the differences ofadaptation and evolution and further explain how they are related. The results of thestudy show that adaptation and evolution have become more entwined with growthof interest to self-managing dynamic software.
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Ayara, Modupe. "Using evolutionary computation to find robust strategies for software agents in online auctions /." Leeds, 2001. http://www.leeds.ac.uk/library/counter2/compstmsc/20002001/ayara.ps.
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Cryan, Mary Elizabeth. "Learning and approximation algorithms for problems motivated by evolutionary trees." Thesis, University of Warwick, 1999. http://wrap.warwick.ac.uk/61093/.
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In this thesis we consider some computational problems motivated by the biological problem of reconstructing evolutionary trees. In this thesis, we are concerned with the design and analysis of efficient algorithms for clearly defined combinatorial problems motived by this application area. We present results for two different kinds of problem. Our first problem is motivated by models of evolution that describe the evolution of biological species in terms of a stochastic process that alters the DNA of species. The particular stochastic model that we considered is called the Two-State General Markov Model. In this model, an evolutionary tree can be associated with a distribution on the different "patterns" that may appear among the sequences for all the species in the evolutionary tree. Then the data for a collection of species whose evolutionary tree is unknown can be viewed as samples from this (unknown) distribution. An interesting problem asks whether we can use samples from an unknown evolutionary tree M to find another tree M*for those species, so that the distribution of M* is similar to that of M. This is essentially a PAC-learning problem ("Probably Approximately Correct") in the sense of Valiant and Kearns et al. Our results show that evolutionary trees in the Two-State General Markov can be efficiently PAC-learned in the variation distance metric using a "reasonable" number of samples. The two other problems that we consider are combinatorial problems that are also motivated by evolutionary tree construction. The input to each of these problems consists of a fixed tree topology whose leaves are bijectively labelled by the elements of a species set, as well as data for those species. Both problems involve labelling the internal nodes in the fixed topology in order to minimize some function on that tree (both functions that we consider are assumed to test the quality of the tree topology in some way). The two problems that we consider are known to be NP-hard. Our contribution is to present efficient approximation algorithms for both problems.
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Churchill, Alexander Wainwright. "Evolutionary approaches to optimisation in rough machining." Thesis, University of Sussex, 2014. http://sro.sussex.ac.uk/id/eprint/48902/.
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This thesis concerns the use of Evolutionary Computation to optimise the sequence and selection of tools and machining parameters in rough milling applications. These processes are not automated in current Computer-Aided Manufacturing (CAM) software and this work, undertaken in collaboration with an industrial partner, aims to address this. Related research has mainly approached tool sequence optimisation using only a single tool type, and machining parameter optimisation of a single-tool sequence. In a real world industrial setting, tools with different geometrical profiles are commonly used in combination on rough machining tasks in order to produce components with complex sculptured surfaces. This work introduces a new representation scheme and search operators to support the use of the three most commonly used tool types: end mill, ball nose and toroidal. Using these operators, single-objective metaheuristic algorithms are shown to find near-optimal solutions, while surveying only a small number of tool sequences. For the first time, a multi-objective approach is taken to tool sequence optimisation. The process of ‘multi objectivisation' is shown to offer two benefits: escaping local optima on deceptive multimodal search spaces and providing a selection of tool sequence alternatives to a machinist. The multi-objective approach is also used to produce a varied set of near-Pareto optimal solutions, offering different trade-offs between total machining time and total tooling costs, simultaneously optimising tool sequences and the cutting speeds of individual tools. A challenge for using computationally expensive CAM software, important for real world machining, is the time cost of evaluations. An asynchronous parallel evolutionary optimisation system is presented that can provide a significant speed up, even in the presence of heterogeneous evaluation times produced by variable length tool sequences. This system uses a distributed network of processors that could be easily and inexpensively implemented on existing commercial hardware, and accessible to even small workshops.
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Senft, Björn [Verfasser]. "A value-centered software engineering approach for unique and novel software-based solutions : aligning design thinking with a coopetition-based evolutionary software development / Björn Senft." Paderborn : Universitätsbibliothek, 2021. http://d-nb.info/1236630041/34.
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Wadhwani, Vickey, and Shoain Ahmed. "Evaluating Evolutionary Prototyping for Customizable Generic Products in Industry." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2402.
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Software products can be categorized into three types namely bespoke, market driven and customizable generic products. Each of these products is facing different problems in their development and to address these problems different software process models have been introduced. The use and validation of different software process models for bespoke and market driven products have been discussed in earlier work. On the other hand, less attention was paid to the customizable generic products. Our thesis will fill this gap by conducting a case study on evolutionary prototyping (EP) for customizable generic products. The main aim of the thesis is to make an initial validation of EP for customizable generic products. In order to fulfill the aforementioned aim we performed a literature study on prototyping and EP, together with development of two customizable generic products. During this development process, we used approach of EP. The results from our investigation will provide researchers and practitioners with a deep insight to the EP and also to guide them in making decision regarding the use of EP. The main findings from our investigation are as follows: • EP is not used standalone as a software process model. Rather it is used as a concept that can be augmented with some iterative software process model. • Negative and positive aspects of EP were highlighted by discussing situations where it could be a better choice, with its advantages and disadvantages. • An initial validation was performed on EP for customizable generic products. Reported results from this case study show that the selected approach is a good choice when you want to have innovative product, clear ambiguous and sketchy requirements, discover new requirements, save resources of software testing, involve and satisfy customer. EP shows vulnerabilities in documentation of product and quality of code.Mobile number : 0046762183249
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Schairer, Axel [Verfasser]. "Transformations of specifications and proofs to support an evolutionary formal software development / Axel Schairer." Aachen : Shaker, 2006. http://d-nb.info/981172156/34.
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Rawlings, Tomas Richard. "Network media distribution systems: understanding the media ecology of software using an evolutionary framework." Thesis, University of the West of England, Bristol, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.601228.
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Developed from the practice of building an open source Peer-to-Peer (P2P) distribution system, this research begins by examining the potential forking (duplicating two copies as separate entities) of a software development project. This research begins with the exploration of the discourses of forking within Software Studies and Actor Network Theory, concluding that both offer a degree of insight, especially around power relations but that they both lack predictive and temporal qualities in order to model the emergent pattems arising from the development process. As an alternative view, the research considers the idea of forking as a software-borne process of speciation, where forking establishes a barrier to source-code flow much as a geographical barrier impedes genetic exchange in biology. Exploring the existing ideas and theories of applying an evolutionary model to the development of human technology it argues that these fall into two broad areas of concern. The first of these areas considers evolution as too narrow a mechanistic process and so fails to fully account for the role of human society and culture. The second of these areas uses biological evolution as a metaphorical construct and by doing so fails to establish any solid methodological framework beyond that proxy. The research adopts the position that to fully examine technology evolution, the discourse should involve examples of technology which encompass complex assemblages of culture and technology. The research cites media as an example of such as technology and P2P as a sub-set of a media technology. Responding to the concerns with existing concepts of technology evolution, the research draws on the practice of software development to arrive at a new methodology. This method begins with the position that P2P technology has many advantages as an object of study much as biologists study fruit flies for genetic research, where P2P is analogous to studying the genotype (genetic make-up) and phenotype (visual appearance) of a biological species. Building on this idea it then argues that the source code used to create software is a parallel of DNA in form and function. From this basis the research proposes how examining the layers of committed source code of software projects can be used to establish a common analytical framework. This method allows the data gathered from this process to depict proto-phylogenetic trees of different P2P projects, giving an insight into how software forking as a form of technology speciation. It concludes that there is a form of an evolutionary process occurring within software development and outlines a hypotheSiS as to how this form operates. This hypothesis proposes that the environment is the landscape conSisting of human culture and societies, whereas the individual organism here is the particular iteration of the technology and an evolutionary process is emergent from this interaction. It then explores some predictive uses of this hypothesis.
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Yano, Thaise 1979. "Uma abordagem evolutiva multiobjetivo para geração automática de casos de teste a partir de máquinas de estados." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275732.
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Orientador: Eliane MartinsTese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: A geração automática de casos de teste contribui tanto para melhorar a produtividade quanto para reduzir esforço e custo no processo de desenvolvimento de software. Neste trabalho é proposta uma abordagem, denominada MOST (Multi-Objective Search-based Testing approach from EFSM), para gerar casos de teste a partir de Máquina de Estados Finitos Estendida (MEFE) com a aplicação de uma técnica de otimização. No teste baseado em MEFE, é necessário encontrar uma sequência de entrada para exercitar um caminho no modelo, a fim de cobrir um critério de teste (e.g. todas as transições). Como as sequências podem ter diferentes tamanhos, motivou-se o desenvolvimento do algoritmo M-GEOvsl (Multi-Objective Generalized Extremal Optimization with variable string length) que permite gerar soluções de diferentes tamanhos. Além disso, por ser um algoritmo multiobjetivo, M-GEOvsl também possibilita que mais de um critério seja usado para avaliar as soluções. Com a aplicação desse algoritmo em MOST, tanto a cobertura da transição alvo quanto o tamanho da sequência são levados em consideração na geração de casos de teste. Para guiar a busca, são utilizadas informações das dependências do modelo. O algoritmo gera as sequências de entrada, incluindo os valores de seus parâmetros. Em MOST, um modelo executável da MEFE recebe como entrada os dados gerados pelo M-GEOvsl e produz dinamicamente os caminhos percorridos. Uma vez que os aspectos de controle e dados do modelo são considerados durante a execução do modelo, evita-se o problema de geração de caminhos infactíveis. Um caminho pode ser sintaticamente possível, mas semanticamente infactível, devido aos conitos de dados envolvidos no modelo. Para avaliar a abordagem proposta foram realizados vários experimentos com modelos da literatura e de aplicações reais. Os resultados da abordagem também foram comparados com os casos de teste obtidos em um trabalho relacionado.
Abstract: Automated test case generation can improve the productivity as well as reduce effort and cost in the software development process. In this work an approach, named MOST (Multi- Objective Search-based Testing approach from EFSM), is proposed to generate test cases from Extended Finite State Machine (EFSM) using an optimization technique. In EFSM based testing, an input sequence should be found to sensitize a path in the model, in order to cover a test criterion (e.g. all transitions). As the sequences can have different lengths, it motivates the development of the M-GEOvsl (Multi-Objective Generalized Extremal Optimization with variable string length) algorithm that makes possible the generation of solutions with different lengths. Moreover, as a multiobjective algorithm, M-GEOvsl also allows to use more than one criterion to evaluate the solutions. Using this algorithm in MOST, the coverage of the target transition as well as the sequence length are taken into account in the test case generation. To guide the search, the information about the model dependences is used. The algorithm generates the input sequences, including the values of their parameters. In MOST, an executable model of the EFSM receives as input the data generated by M-GEOvsl and produces the traversed paths dynamically. Since the control and data aspects are considered during model execution, the problem of infeasible path generation is avoided. A path can be syntatically possible, but semantically infeasible, due to the data conicts in the model. In order to evaluate the proposed approach, experiments were performed with models of the literature and real-world applications. The results were also compared to the test cases obtained in a related work
Doutorado
Ciência da Computação
Doutor em Ciência da Computação
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Urquhart, Neil B. "Solving real-world routing problems using evolutionary algorithms and multi-agent-systems." Thesis, Edinburgh Napier University, 2003. http://researchrepository.napier.ac.uk/Output/2748.
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This thesis investigates the solving of routing problems using Evolutionary Algorithms (EAs). Routing problems are known to be hard and may possess complex search spaces. Evolutionary algorithms are potentially powerful tools for finding solutions within complex search spaces. The problem investigated is the routing of deliveries to households within an urban environment; the most common instance of this problem is that of daily postal deliveries. A representation known as Street Based Routing (SBR) is presented. This is a problem representation that makes use of the real world groupings of streets and houses. This representation is an indirect problem representation designed specifically for use with EAs. The SBR representation is incorporated within an EA and used to construct delivery routes around a variety of problem instances. The EA based system is compared against a Travelling Salesman Problem (TSP) solver, and the results are presented. The EA based system produces routes that are on average slightly longer than those produced by the TSP solver. Real world problems may often involve the construction of a network of delivery routes that are subject to multiple hard and soft constraints. A Multi Agent System (MAS) based framework for building delivery networks is presented that makes use of the SBR based EA presented earlier. Each agent within the system uses an EA to construct a single route. Agents may exchange work (via auctions or by directly negotiated exchanges) allowing the optimisation of their route. It is demonstrated that this approach has much potential and is capable of constructing delivery networks meeting set constraints, over a range of problem instances and constraint values.
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Collins, Trevor. "The application of software visualization technology to evolutionary computation : a case study in Genetic Algorithms." Thesis, Open University, 1998. http://oro.open.ac.uk/28579/.
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Evolutionary computation is an area within the field of artificial intelligence that is founded upon the principles of biological evolution. Evolution can be defined as the process of gradual development. Evolutionary algorithms are typically applied as a generic problem solving method, searching a problem space in order to locate good solutions. These solutions are found through an iterative evolutionary search that progresses by means of gradual developments. In the majority of cases of evolutionary computation the user is not aware of their algorithm's search behaviour. This causes two problems. First, the user has no way of assuring the quality of any solutions found other than to compare the solutions found by the algorithm with any available benchmark solutions or to re-run the algorithm and check if the results can be repeated or improved upon. Second, because the user is unaware of the algorithm's behaviour they have no way of identifying the contribution of the different components of the algorithm and therefore, no direct way of analyzing the algorithm's design and assigning credit to good algorithm components, or locating and improving ineffective algorithm components. The artificial intelligence and engineering communities have been slow to accept evolutionary computation as a robust problem-solving method because, unlike cased-based systems, rule-based systems or belief networks, they are unable to follow the algorithm's reasoning when locating a set of solutions in the problem space. During an evolutionary algorithm's execution the user may be able to see the results of the search but the search process itself like is a "black box" to the user. It is the search behaviour of evolutionary algorithms that needs to be understood by the user, in order for evolutionary computation to become more accepted within these communities. The aim of software visualization is to help people understand and use computer software. Software visualization technology has been applied successfully to illustrate a variety of heuristic search algorithms, programming languages and data structures. This thesis adopts software visualization as an approach for illustrating the search behaviour of evolutionary algorithms. Genetic Algorithms ("GAs") are used here as a specific case study to illustrate how software visualization may be applied to evolutionary computation. A set of visualization requirements are derived from the findings of a GA user study. A number of search space visualization techniques are examined for illustrating the search behaviour of a GA. "Henson," an extendable framework for developing visualization tools for genetic algorithms is presented. Finally, the application of the Henson framework is illustrated by the development of "Gonzo," a visualization tool designed to enable GA users to explore their algorithm's search behaviour. The contributions made in this thesis extend into the areas of software visualization, evolutionary computation and the psychology of programming. The GA user study presented here is the first and only known study of the working practices of GA users. The search space visualization techniques proposed here have never been applied in this domain before, and the resulting interactive visualizations provide the GA user with a previously unavailable insight into their algorithm's operation.
34
Hall, Kathleen J. "An examination of issues related to the implementation of evolutionary development and DOD-STD-2167A in software development : a practitioners view /." This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-03302010-020143/.
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GUIMARAES, THIAGO SOUZA MENDES. "SUPPORT TO THE SYNTHESIS OF STRUCTURAL MODELS OF OBJECT-ORIENTED SOFTWARE USING CO-EVOLUTIONARY GENETIC ALGORITHMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7337@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOREsta dissertação investiga o uso de Algoritmos Genéticos Co-evolucionários na automatização do processo de desenvolvimento de Sistemas de Software Orientados a Objetos. A qualidade final do software depende principalmente da qualidade da modelagem desenvolvida para o mesmo. Durante a fase de modelagem, diversos modelos são desenvolvidos antecipando diversas visões do produto final, e possibilitando a avaliação do software antes mesmo que ele seja implementado. A síntese de um modelo de software pode, portanto, ser vista como um problema de otimização onde se busca uma melhor configuração entre os elementos contemplados pelo paradigma de orientação a objetos, como classes, métodos e atributos, que atenda a critérios de qualidade de design. O objetivo do trabalho foi estudar uma forma de sintetizar modelagens de maior qualidade através da evolução por Algoritmos Genéticos Co- evolucionários. Para avaliar a modelagem do software, foram investigadas métricas de qualidade de software tais como: Reutilização, Flexibilidade, Inteligibilidade, Funcionalidade, Extensibilidade e Efetividade. Essas métricas foram aplicadas na função de avaliação, que por sua vez, foi definida objetivando a síntese de uma modelagem de software orientado a objetos com uma maior qualidade. Neste problema, deseja-se contemplar mais de um objetivo ao mesmo tempo. Para isso, foi utilizada a técnica de Pareto para problemas multi- objetivos. Os resultados obtidos foram comparados com modelagens produzidas por especialistas e as suas características analisadas. O desempenho do AG no processo de otimização foi comparado com o da busca aleatória e, em todos os casos, os resultados obtidos pelo modelo foram sempre superiores.
This work investigates the use of Co-evolutionary Genetic Algorithms in the automation of the development process of object-oriented software systems. The software final quality depends mainly on the design quality developed for the same. During the design phase, different models are developed anticipating various visions of the end product, thus making possible the software evaluation before it is implemented. The synthesis of a software model can, therefore, be seen as an optimization problem where it seeks a better configuration between the contemplated elements for the object-oriented paradigm, as classes, methods and attributes, which follows the quality design criteria. The work goal was to study a way to synthesize designs of better quality through its evolution by Coevolutionary Genetic Algorithms. In order to assess the software quality, it was also investigated software quality metrics, such as: Reusability, Flexibility, Understandability, Functionality, Extensibility and Effectiveness. These metrics were applied in an evaluation function that, in turn, was defined aiming at the object-oriented design synthesis with a better quality. In this problem, it is desired to contemplate more than one objective at a time. For this, the Pareto technique for multi-objective problems was used. The results were compared with designs produced by specialists and its characteristics analyzed. The GA performance in the optimization process was compared with the exhaustive search and, in all cases, the model results were superior.
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Pickardt, Christoph W. "Evolutionary methods for the design of dispatching rules for complex and dynamic scheduling problems." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/59757/.
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Three methods, based on Evolutionary Algorithms (EAs), to support and automate the design of dispatching rules for complex and dynamic scheduling problems are proposed in this thesis. The first method employs an EA to search for problem instances on which a given dispatching rule performs badly. These instances can then be analysed to reveal weaknesses of the tested rule, thereby providing guidelines for the design of a better rule. The other two methods are hyper-heuristics, which employ an EA directly to generate effective dispatching rules. In particular, one hyper-heuristic is based on a specific type of EA, called Genetic Programming (GP), and generates a single rule from basic job and machine attributes, while the other generates a set of work centre-specific rules by selecting a (potentially) different rule for each work centre from a number of existing rules. Each of the three methods is applied to some complex and dynamic scheduling problem(s), and the resulting dispatching rules are tested against benchmark rules from the literature. In each case, the benchmark rules are shown to be outperformed by a rule (set) that results from the application of the respective method, which demonstrates the effectiveness of the proposed methods.
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Guney, Tacettin Dogacan. "Prediction Of Protein-protein Interactions From Sequence Using Evolutionary Relations Of Proteins And Species." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611058/index.pdf.
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Prediction of protein-protein interactions is an important part in understanding the biological processes in a living cell. There are completely sequenced organisms that do not yet have experimentally verified protein-protein interaction networks. For such organisms, we can not generally use a supervised method, where a portion of the protein-protein interaction network is used as training set. Furthermore, for newly-sequenced organisms, many other data sources, such as gene expression data and gene ontology annotations, that are used to identify protein-protein interaction networks may not be available. In this thesis work, our aim is to identify and cluster likely protein-protein interaction pairs using only sequence of proteins and evolutionary information. We use a protein&rsquos phylogenetic profile because the co-evolutionary pressure hypothesis suggests that proteins with similar phylogenetic profiles are likely to interact. We also divide phylogenetic profile into smaller profiles based on the evolutionary lines. These divided profiles are then used to score the similarity between all possible protein pairs. Since not all profile groups have the same number of elements, it is a difficult task to assess the similarity between such pairs. We show that many commonly used measures do not work well and that the end result greatly depends on the type of the similarity measure used. We also introduce a novel similarity measure. The resulting dense putative interaction network contains many false-positive interactions, therefore we apply the Markov Clustering algorithm to cluster the protein-protein interaction network and filter out the weaker edges. The end result is a set of clusters where proteins within the clusters are likely to be functionally linked and to interact. While this method does not perform as well as supervised methods, it has the advantage of not requiring a training set and being able to work only using sequence data and evolutionary information. So it can be used as a first step in identifying protein-protein interactions in newly-sequenced organisms.
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Hall, Kathleen J. "An examination of issues related to the implementation of evolutionary development and DOD-STD-2167A in software development:a practitioners view." Master's thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/41813.
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The author of this report has long been interested in the
process of developing software. Commonly referred to as
"software engineering", the definition and standardization of
the process has been a subject of concern throughout the
software industry. As early as 1972, Frederick P. Brooks
likened large system programming efforts to the La Brea Tar
Pits; the muck, mire and tar of software development delays
and cost overruns eventually defeating even the most skillful
and experienced developers. [1] This report is intended to
provide another stepping stone through the tar.
The author was fortunate to be provided the opportunity to develop two similar software projects, in like hardware environments, using different development approaches. The first project was developed using an Evolutionary Development approach, the later system utilized the current government standard, DOD-STD-2167A. This report outlines the lessons learned in applying these development approaches.
This report does not intend to criticize either the Evolutionary Development or DOD-STD-2167A approaches to
developing software. Rather this analysis seeks to identify
factors and issues which software developers should consider
in implementing either of these approaches.
Master of Science
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Frey, Sören [Verfasser]. "Conformance Checking and Simulation-based Evolutionary Optimization for Deployment and Reconfiguration of Software in the Cloud / Sören Frey." Kiel : Universitätsbibliothek Kiel, 2014. http://d-nb.info/1133542972/34.
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Minařík, Miloš. "Souběžný evoluční návrh hardwaru a softwaru." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-412594.
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Genetické programování (GP) je v určitém rozsahu schopno automaticky generovat požadované programy, aniž by uživatel musel určit, jakým způsobem má program postupovat. GP bylo s úspěchem použito k řešení široké škály praktických problémů z různých oblastí, přičemž výsledky byly často srovnatelné s řešeními vytvořenými člověkem. Doposud však nebyla zodpovězena otázka, zda GP dokáže generovat vysoce optimalizovaný výpočetní model (platformu) spolu s programem spustitelným na této platformě, který by řešil daný problém při dodržení všech omezení (například na plochu na čipu a zpoždění). V případě scénářů, kdy je optimalizováno více kritérií, by uživatelským výstupem měla být množina nedominovaných řešení s různými kombinacemi úrovně využití zdrojů (plocha, příkon) a výkonu (rychlosti provádění). Tento problém může být chápán jako souběžný návrh hardwaru a softwaru, zkráceně HW/SW codesign. Tato práce zkoumá způsoby, jakými lze souběžně evolučně vyvíjet platformu a programy v případě, že je problém zadán množinou vektorů vstupů a jim odpovídajících výstupů. Nejprve byl vytvořen model architektury a evoluční platforma zajišťující zpracování a evoluční vývoj těchto architektur. Kandidátní mikroprogramové architektury byly evolvovány spolu s programy pomocí lineárního genetického programování. Následně byla provedena série jednodušších experimentů. Navržená platforma dosahovala výsledků srovnatelných s nejnovějšími metodami. Na základě slabých míst objevených během počátečních experimentů byla platforma rozšířena. Rozšířená platforma byla poté ověřena na několika složitějších experimentech. Jeden z nich byla zaměřen na efektivní implementaci aproximace sigmoidální funkce. Platforma v tomto případě našla řadu různých řešení implementujících aproximaci sigmoidy, z nichž některá byla sekvenční a jiná čistě kombinační. V rámci experimentu byly evolučně nalezeny i známé algoritmy, přičemž některé z nich byly evolucí dokonce optimalizovány pro podmnožinu definičního oboru zvolenou pro daný experiment. Poslední sada experimentů byla zaměřena na evoluční návrh obrazových filtrů pro redukci šumu typu sůl a pepř. Platforma v tomto případě znovuobjevila koncept přepínaných filtrů a naezla variantu přepínaného mediánového filtru, která byla z hlediska výsledků filtrace srovnatelná s běžně používanými metodami. Tato práce prokázala, že pomocí genetického programování lze navrhovat a optimalizovat malé HW/SW systémy. Automatizovaný evoluční návrh složitějších HW/SW systémů zůstává otevřeným problémem vhodným k dalšímu výzkumu.
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Bac, Joanna Ewa. "Software intelligence (SI), dependent legal personhood & SI-human amalgamation : an evolutionary step for US patent law and SI." Thesis, University of Aberdeen, 2018. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=237873.
Full textAbstract:
This doctoral thesis addresses the question of why and how the United States of America (US) legal system should grant legal personhood to software intelligence (SI). This new legal status of SI is visualised as a dependent type of person. The SI dependent legal person would be determined by an inextricable connection between SI and a new type of corporate body, introduced here as SI-Human Amalgamation (SIHA). SI has been defined as one or more computer programmes with an ability to create work that is unforeseen by humans. This includes SI capacity to generate unforeseen innovations, patentable inventions and/or infringe the rights of other patent holders. At present, SI is an entity unrecognised by law. The fact that SI is neither a natural nor a legal person indicates that it cannot be considered the rights' owner or liability bearer. This in turn creates tensions both in society and legal systems because questions, such as, who should hold those rights or be liable for autonomous acts of SI, remain unanswered. It is argued that the SI dependent legal person and SIHA, are necessary to address the new challenges introduced by SI. SI and SIHA, their creativity and actions would be distinct from those performed by human beings involved in the creation of this amalgamation, such as SI's operators or programmers. As such, this structure would constitute an amalgamation based on human beings and SI cooperation (SIHA). SI, as a dependent legal person, would hold the patents rights to its own inventions thus ensuring favourable conditions for the incentives of the US patent system. In addition, the proposed legal framework with the use of legislative instruments could address any liability concerns arising from the foreseen and unforeseen actions, omissions and failure to act of SI.
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Murdock, J. William. "Self-improvment through self-understanding : model-based reflection for agent adaptation." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/8225.
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Pečínka, Zdeněk. "Gramatická evoluce v optimalizaci software." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363820.
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This master's thesis offers a brief introduction to evolutionary computation. It describes and compares the genetic programming and grammar based genetic programming and their potential use in automatic software repair. It studies possible applications of grammar based genetic programming on automatic software repair. Grammar based genetic programming is then used in design and implementation of a new method for automatic software repair. Experimental evaluation of the implemented automatic repair was performed on set of test programs.
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Kakar, Adarsh Kumar. "Feature selection for evolutionary commercial-off-the-shelf software| Studies focusing on time-to-market, innovation and hedonic-utilitarian trade-offs." Thesis, The University of Alabama, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3596169.
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Feature selection is one of the most important decisions made by product managers. This three article study investigates the concepts, tools and techniques for making trade-off decisions of introducing new features in evolving Commercial-Off-The-Shelf (COTS) software products. The first article investigates the efficacy of various feature selection techniques when the trade-off is between comprehensiveness and time-to-market. The second article investigates the impact of current level of product performance when the trade-off is between providing different types of innovative features to the users. The third article investigates the impact on the ability of the COTS product to attract new users and retain existing users when the trade-off is between providing utilitarian and hedonic value through new product features.
To meet these research goals an extensive multidisciplinary study of Information Systems (IS) and Product Development literatures was conducted followed by experimental research. The experiments were conducted among youth between 19-24 years who were users of Gmail software and produced some key findings.
In the first study the Kano survey method was found to be effective in identifying those features which added value to the product and those that did not. This finding will facilitate product managers in using appropriate techniques for identifying the critical product features to be built into the COTS product thereby reducing time-to-market without sacrificing product quality. In the second study, current COTS product performance was found to significantly impact the type of innovation to be introduced into the COTS product. Basic or Core product innovations were found to have value for the users when performance is low but not when the performance is high. On the other hand, Expected or product Performance innovations and Augmented or user Excitement innovations were found to have value when the performance is high but not when the performance is low. In the third study, Hedonic value and Utilitarian value of product features were found to have distinctive impact on users. While Hedonic value impacted Word-of-Mouth, a measure of the products' capacity to attract new customers, Utilitarian value impacted User Loyalty, a measure of the products' capacity to retain existing customers.
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Buzzo, André Vinicius. "Estudo de algoritmo evolutivo com codificação real na geração de dados de teste estrutural e implementação de protótipo de ferramenta de apoio." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275761.
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Orientador: Eliane MartinsDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: A geração automática de dados de teste pode ser abordada como um problema de otimização e algoritmos evolutivos se tornaram um foco de muita pesquisa nesta área. Recentemente um novo tipo de algoritmo evolutivo chamado GEO (GEO - Generalized Extremal Optimization) tem sido explorado em uma grande classe de problemas de otimização. Neste trabalho é apresentado o uso do algoritmo evolutivo GEO com codificação real - GEOreal - na geração de dados de teste. O desempenho deste algoritmo é comparado com diversos outros algoritmos e para melhor avaliar os resultados, duas funções objetivo - que mapeiam o problema de geração de dados em um problema de otimização - foram utilizadas. O algoritmo GEOreal combinado com a função objetivo Bueno e Jino obtiveram os melhores resultados nos problemas abordados. Um protótipo foi desenvolvido implementando todos os conceitos envolvidos neste trabalho e o seu desempenho foi comparado com outras ferramentas já disponíveis no mercado. Os resultados mostraram que este protótipo superou as ferramentas comparadas ao minimizar o tempo dispendido no esforço de gerar os dados de teste
Abstract: Automatic test data generation can be approached as an optimization problem and evolutionary algorithms have become a focus of much research in this area. Recently a new type of evolutionary algorithm called GEO (GEO - Generalized Extremal Optimization) has been explored in a large class of optimization problems. This paper presents the use of evolutionary algorithm with real coding GEO - GEOreal - in test data generation. The performance of this algorithm is compared with several other algorithms and to better compare the results two objective functions - that map the problem of generating data in an optimization problem - were used. The algorithm GEOreal combined with the function Bueno and Jino had the best results in the problems addressed. A prototype was developed implementing all the concepts involved in this work and its performance was compared with other tools already available. The results showed that this prototype was better than the compared tools when minimizing the time spent in the effort to test data generation
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
46
Aldogan, Deniz. "Memetic Algorithms For Timetabling Problems In Private Schools." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606218/index.pdf.
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The aim of this study is to introduce a real-world timetabling problem that exists in some private schools in Turkey and to solve such problem instances utilizing memetic algorithms.
Being a new type of problem and for privacy reasons, there is no real data available. Hence for benchmarking purposes, a random data generator has been implemented. Memetic algorithms (MAs) combining genetic algorithms and hill-climbing are applied to solve synthetic problem instances produced by this generator.
Different types of recombination and mutation operators based on the hierarchical structure of the timetabling problem are proposed. A modified version of the violation directed hierarchical hill-climbing method (VDHC), introduced by A. Alkan and E. Ozcan, coordinates the process of 12 different low-level hill-climbing operators grouped in two distinct arrangements that attempt to resolve corresponding constraint violations. VDHC is an adaptive method advocating cooperation of hill-climbing operators. In addition, MAs with VDHC are compared with different versions of multimeme algorithms and pure genetic algorithms.
Experimental results on synthetic benchmark data set indicate the success of the proposed MA.
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Hagnell, Christian, and Mosanen Kiavosh Saidi. "Topology optimization : A comparison between the SIMP and BESO methods using open-source software." Thesis, Uppsala universitet, Tillämpad mekanik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447869.
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Structural optimization is a useful tool for engineers and designers in construction technology as well asvehicle and mechanical engineering. With structure optimization, a computer can, with the help of finiteelement analysis, calculate the smallest possible amount of material needed to meet the requirements onthe part to be produced.The purpose of this report is to use two different implementations for finite element calculations fortopology optimization of a beam. Results from the optimizations will then be 3D printed with differentsettings. The beam will be tested for displacement, stress and strain in a universal testing machine. Theresults from the experiment will be compared with computed simulations of the same beam.For the structural optimization, two methods are used and compared: Solid Isotropic Material withPenalization and Bidirectional Evolutionary Structural Optimization. A total of eight beams, four fromeach method, were printed with a 3D printer with two different positions on the printer bed and withdifferent degrees of infill ratios. These were tested with a machine that could register both pressure anddeformation and were filmed to be able to see the strain. The deformation of the beams was alsosimulated in a software computer program to see what deformation difference there was betweenexperiment and reality.It turned out that the beams that were printed behaved anisotropic even though solid plastic should beincluded among isotropic materials. The deformation of the model looked like the finite elementcalculation, but the actual deformation was significantly larger than what was calculated by the computersoftware.
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Abreu, Bruno Teixeira de. "Uma abordagem evolutiva para a geração automatica de dados de teste." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276245.
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Orientadores: Eliane Martins, Fabiano Luis de SousaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: O teste é uma atividade importante do processo de desenvolvimento de software, e automatizar a geração de dados de teste contribui para a redução dos esforços de tempo e recursos. Recentemente foi mostrado que os algoritmos evolutivos, tal como os Algoritmos Genéticos (AGs), são ferramentas valiosas para a geração de dados. Este trabalho avalia pela primeira vez o desempenho de um algoritmo evolutivo proposto recentemente, a Otimização Extrema Generalizada (em inglês, Generalized Extremal Optimization, GEO), na geração de dados de teste para cobrir um subconjunto de caminhos de um programa, com ou sem loops. Sete programas muito conhecidos e utilizados como benchmarks por outros autores foram escolhidos como estudos de caso, e o desempenho do GEO foi comparado com o de um AG e o Random-Test (RT). Uma aplicação real do Instituto Nacional de Pesquisas Espaciais (INPE) também foi testada para validar a pesquisa, e as comparações de desempenho incluíram uma variação do AG utilizado nos benchmarks. Para os benchmarks e a aplicação real, o uso do GEO exigiu muito menos esforço computacional para gerar os dados do que os AGs, e a cobertura média de caminhos obtida por ele foi muito semelhante à dos AGs. Além disso, o GEO também exigiu muito menos esforço computacional no ajuste interno de parâmetros do que os AGs. Estes resultados indicam que o GEO é uma opção muito atraente a ser utilizada na geração de dados de teste
Abstract: Software testing is an important activity of the software development process and automating test data generation contributes to reduce cost and time efforts. It has recently been shown that evolutionary algorithms, such as the Genetic Algorithms (GAs), are valuable tools for test data generation. This work assesses for the first time the performance of a recently proposed evolutionary algorithm, the Generalized Extremal Optimization (GEO), on generating test data to cover a subset of paths of a program, with or without loops. Seven well known benchmark programs were used as study cases, and the performance of GEO was compared to the one of a GA and Random-Test (RT). A real application from Instituto Nacional de Pesquisas Espaciais (INPE) was also tested in order to validate the research, and the performance comparisons included one variation of the GA used in the benchmarks. For the benchmark programs and the real application, using GEO required much less computational effort to generate test data than using the GAs, and GEO¿s average coverage was very similar to GA¿s. Besides this, it also required much less computational effort on internal parameter setting than the GAs. These results indicate that GEO is a very attractive option to be used for test data generation
Mestrado
Mestre em Ciência da Computação
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Bibai, Jacques. "Segmentation et évolution pour la planification : le système Divide-And-Evolve." Phd thesis, Université Paris Sud - Paris XI, 2010. http://tel.archives-ouvertes.fr/tel-00544407.
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DAEX is a metaheuristic designed to improve the plan quality and the scalability of an encapsulated planning system. DAEX is based on a state decomposition strategy, driven by an evolutionary algorithm, which benefits from the use of a classical planning heuristic to maintain an ordering of atoms within the individuals. The proof of concept is achieved by embedding the domain-independent satisficing YAHSP planner and using the critical path h1 heuristic. Experiments with the resulting algorithm are performed on a selection of IPC benchmarks from classical, cost-based and temporal domains. Under the experimental conditions of the IPC, and in particular with a universal parameter setting common to all domains, DAEYAHSP is compared to the best planner for each type of domain. Results show that DAEYAHSP performs very well both on coverage and quality metrics. It is particularly noticeable that DAEX improves a lot on plan quality when compared to YAHSP, which is known to provide largely suboptimal solutions, making it competitive with state-of-the-art planners. This article gives a full account of the algorithm, reports on the experiments and provides some insights on the algorithm behavior.
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Alem, Mohammad. "Event-based risk management of large scale information technology projects." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/11392.
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Globalisation has come as a double-edged blade for information technology (IT) companies; providing growth opportunities and yet posing many challenges. Software development is moving from a monolithic model to a distributed approach, where many entities and organisations are involved in the development process. Risk management an important area to deal with all the kinds of technical and social issues within companies planning and programming schedules, and this new way of working requires more attention to be paid to the temporal, socio-cultural and control aspects than before. Multinational companies like IBM have begun to consider how to address the distributed nature of its projects across the globe. With outlets across the globe, the company finds various people of different cultures, languages and ethics working on a single and bigger IT projects from different locations. Other IT companies are facing the same problems, despite there being many kinds of approaches available to handle risk management in large scale IT companies. IBM commissioned the Distributed Risk Management Process (DRiMaP) model as a suitable solution. This model focused on the collaborative and on-going control aspects, and paid attention to the need for risk managers, project managers and management to include risk management into all phases of projects and the business cycle. The authors of the DRiMaP model did not subject it to extensive testing. This research sets out to evaluate, improve and extend the model process and thereby develop a new and dynamic approach to distributed information systems development. To do this, this research compares and contrasts the model with other risk management approaches. An Evolutionary Model is developed, and this is subjected to empirical testing through a hybrid constructive research approach. A survey is used to draw out the observations of project participants, a structured interview gathered the opinions of project experts, a software tool was developed to implement the model, and SysML and Monte Carlo methods were applied to this to simulate the functioning of the model. The Evolutionary Model was found to partially address the shortcomings of the DRiMaP model, and to provide a valuable platform for the development of an enterprise risk management solution.