Relevant bibliographies by topics / Evolutionary computer algorithms

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Evolutionary computer algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Evolutionary computer algorithms"

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Leciejewski, Sławomir, and Mariusz Szynkiewicz. "Algorithmicity of Evolutionary Algorithms." Studies in Logic, Grammar and Rhetoric 63, no. 1 (2020): 87–100. http://dx.doi.org/10.2478/slgr-2020-0029.

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Abstract In the first part of our article we will refer the penetration of scientific terms into colloquial language, focusing on the sense in which the concept of an algorithm currently functions outside its original scope. The given examples will refer mostly to disciplines not directly related to computer science and to the colloquial language. In the next part we will also discuss the modifications made to the meaning of the term algorithm and how this concept is now understood in computer science. Finally, we will discuss the problem of algorithmicity of evolutionary algorithms, i.e. we w
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Bartz-Beielstein, Thomas, Jürgen Branke, Jörn Mehnen, and Olaf Mersmann. "Evolutionary Algorithms." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4, no. 3 (2014): 178–95. http://dx.doi.org/10.1002/widm.1124.

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Jiang, Dazhi, and Zhun Fan. "The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/474805.

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At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators
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Dioşan, Laura, and Mihai Oltean. "Evolutionary design of Evolutionary Algorithms." Genetic Programming and Evolvable Machines 10, no. 3 (2009): 263–306. http://dx.doi.org/10.1007/s10710-009-9081-6.

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Johannsen, Daniel, Piyush P. Kurur, and Johannes Lengler. "Evolutionary Algorithms for Quantum Computers." Algorithmica 68, no. 1 (2013): 152–89. http://dx.doi.org/10.1007/s00453-013-9784-1.

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Clark, David E., and David R. Westhead. "Evolutionary algorithms in computer-aided molecular design." Journal of Computer-Aided Molecular Design 10, no. 4 (1996): 337–58. http://dx.doi.org/10.1007/bf00124503.

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Li, Kangshun, Fahui Gu, Wei Li, and Ying Huang. "A Dual-Population Evolutionary Algorithm Adapting to Complementary Evolutionary Strategy." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 01 (2018): 1959004. http://dx.doi.org/10.1142/s0218001419590043.

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Optimization problems widely exist in scientific research and engineering practice, which have been one of the research hotshots and difficulties in intelligent computing. The single swarm intelligence optimization algorithms often show such defects as searching stagnation, low accuracy of convergence, part optimum and poor generalization ability when facing the increasingly sophisticated optimization problems. In the study of multiple population, the choice of evolution strategy often has great influence on the performance of the algorithm, and this paper puts forward a kind of dual-populatio
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Beyer, Hans-Georg, Hans-Paul Schwefel, and Ingo Wegener. "How to analyse evolutionary algorithms." Theoretical Computer Science 287, no. 1 (2002): 101–30. http://dx.doi.org/10.1016/s0304-3975(02)00137-8.

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Doerr, Benjamin, Anton Eremeev, Frank Neumann, Madeleine Theile, and Christian Thyssen. "Evolutionary algorithms and dynamic programming." Theoretical Computer Science 412, no. 43 (2011): 6020–35. http://dx.doi.org/10.1016/j.tcs.2011.07.024.

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Ling, Sai Ho, and Hak Keung Lam. "Evolutionary Algorithms in Health Technologies." Algorithms 12, no. 10 (2019): 202. http://dx.doi.org/10.3390/a12100202.

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Health technology research brings together complementary interdisciplinary research skills in the development of innovative health technology applications. Recent research indicates that artificial intelligence can help achieve outstanding performance for particular types of health technology applications. An evolutionary algorithm is one of the subfields of artificial intelligence, and is an effective algorithm for global optimization inspired by biological evolution. With the rapidly growing complexity of design issues, methodologies and a higher demand for quality health technology applicat
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Dissertations / Theses on the topic "Evolutionary computer algorithms"

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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 de
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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 complex
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Abdul, Karim Mohamad Sharis. "Computer-aided aesthetics in evolutionary computer aided design." Thesis, Loughborough University, 2004. https://dspace.lboro.ac.uk/2134/27913.

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This thesis presents research into the possibility of developing a computerised system that can evaluate the aesthetics and engineering aspects of solid shapes. One of the research areas is also to include such an evaluation system into an existing evolutionary CAD system which utilizes the Genetic Algorithms (GAs) technology. An extensive literature survey has been carried out to better understand and clarify the vagueness and subjectivity of the concept of aesthetics, which leads to the work of defining and quantifying a set of aesthetic parameters. This research achieves its novelty in aimi
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Jalalian, Hamid Reza. "Decomposition evolutionary algorithms for noisy multiobjective optimization." Thesis, University of Essex, 2016. http://repository.essex.ac.uk/16828/.

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Multi-objective problems are a category of optimization problem that contain more than one objective function and these objective functions must be optimized simultaneously. Should the objective functions be conflicting, then a set of solutions instead of a single solution is required. This set is known as Pareto optimal. Multi-objective optimization problems arise in many real world applications where several competing objectives must be evaluated and optimal solutions found for them, in the presence of trade offs among conflicting objectives. Maximizing returns while minimizing the risk of s
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Cruz, Alfredo. "Evolutionary Algorithms for VLSI Test Automation." NSUWorks, 2002. http://nsuworks.nova.edu/gscis_etd/472.

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The generation of binary test patterns for VLSI devices belongs to the class of NP complete problems. As the complexity of VLSI circuits increases, the time to generate test vectors becomes computationally expensive. This dissertation focuses on an evolutionary algorithm (EA) approach for the generation of effective test vectors for single and multiple fault detection in VLSI circuits. EAs provide significant speedup while retaining good quality solutions through heuristic procedures. Although not guaranteed to find optimal solution, EAs are able to find very good solutions for a wide range of
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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 M
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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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Baruani, Atumbe Jules. "Network engineering using multi-objective evolutionary algorithms." Thesis, Stellenbosch : Stellenbosch University, 2007. http://hdl.handle.net/10019.1/21548.

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Thesis (MSc)--University of Stellenbosch, 2007.<br>ENGLISH ABSTRACT: We use Evolutionary Multi-Objective Optimisation (EMOO) algorithms to optimise objective functions that reflect situations in communication networks. These include functions that optimise Network Engineering (NE) objective functions in core, metro and wireless sensor networks. The main contributions of this thesis are threefold. Routing and Wavelength Assignment (RWA) for IP backbone networks. Routing and Wavelength Assignment (RWA) is a problem that has been widely addressed by the optical research community. A recent
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Ibrahim, Osman Ali Sadek. "Evolutionary algorithms and machine learning techniques for information retrieval." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/47696/.

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In the context of Artificial Intelligence research, Evolutionary Algorithms and Machine Learning (EML) techniques play a fundamental role for optimising Information Retrieval (IR). However, numerous research studies did not consider the limitation of using EML at the beginning of establishing the IR systems, while other research studies compared EML techniques by only presenting overall final results without analysing important experimental settings such as the training or evolving run-times against IR effectiveness obtained. Furthermore, most papers describing research on EML techniques in IR
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Garcia, Najera Abel. "Multi-Objective evolutionary algorithms for vehicle routing problems." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/1069/.

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The Vehicle Routing Problem, which main objective is to find the lowest-cost set of routes to deliver goods to customers, has many applications in transportation services. In the past, costs have been mainly associated to the number of routes and the travel distance, however, in real-world problems there exist additional objectives. Since there is no known exact method to efficiently solve the problem in polynomial time, many heuristic techniques have been considered, among which, evolutionary methods have proved to be suitable for solving the problem. Despite this method being able to provide
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Books on the topic "Evolutionary computer algorithms"

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Yu, Xinjie. Introduction to evolutionary algorithms. Springer, 2010.

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Ghanea-Hercock, Robert. Applied Evolutionary Algorithms in Java. Springer New York, 2003.

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Drechsler, Rolf. Evolutionary Algorithms for VLSI CAD. Springer US, 1998.

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Drechsler, Rolf. Evolutionary algorithms for VLSI CAD. Kluwer Academic Publishers, 1998.

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Bäck, Thomas. Evolutionary algorithms in theory and practice: Evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, 1996.

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Designing evolutionary algorithms for dynamic environments. Springer, 2004.

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Zelinka, Ivan. Evolutionary Algorithms and Chaotic Systems. Springer-Verlag Berlin Heidelberg, 2010.

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Rothlauf, Franz. Representations for Genetic and Evolutionary Algorithms. Physica-Verlag HD, 2002.

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Mayer, David G. Evolutionary Algorithms and Agricultural Systems. Springer US, 2002.

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Coello, Carlos A. Coello. Evolutionary algorithms for solving multi-objective problems. Kluwer Academic, 2002.

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Book chapters on the topic "Evolutionary computer algorithms"

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Kruse, Rudolf, Christian Borgelt, Christian Braune, Sanaz Mostaghim, and Matthias Steinbrecher. "Fundamental Evolutionary Algorithms." In Texts in Computer Science. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7296-3_13.

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Kruse, Rudolf, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, and Pascal Held. "Fundamental Evolutionary Algorithms." In Texts in Computer Science. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5013-8_13.

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Kruse, Rudolf, Christian Borgelt, Christian Braune, Sanaz Mostaghim, and Matthias Steinbrecher. "Introduction to Evolutionary Algorithms." In Texts in Computer Science. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7296-3_11.

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Kruse, Rudolf, Christian Borgelt, Christian Braune, Sanaz Mostaghim, and Matthias Steinbrecher. "Elements of Evolutionary Algorithms." In Texts in Computer Science. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7296-3_12.

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Kruse, Rudolf, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, and Pascal Held. "Introduction to Evolutionary Algorithms." In Texts in Computer Science. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5013-8_11.

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Kruse, Rudolf, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, and Pascal Held. "Elements of Evolutionary Algorithms." In Texts in Computer Science. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5013-8_12.

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Droste, Stefan, and Dirk Wiesmann. "Metric Based Evolutionary Algorithms." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-46239-2_3.

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Baioletti, Marco, Alfredo Milani, and Valentino Santucci. "Automatic Algebraic Evolutionary Algorithms." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78658-2_20.

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Ebner, Marc. "Engineering of Computer Vision Algorithms Using Evolutionary Algorithms." In Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04697-1_34.

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Reeves, Colin R. "Fitness Landscapes and Evolutionary Algorithms." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10721187_1.

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Conference papers on the topic "Evolutionary computer algorithms"

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Zukai Tang. "Comparison between hierarchical distributed evolutionary algorithms and general distributed evolutionary algorithms." In 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC). IEEE, 2013. http://dx.doi.org/10.1109/mec.2013.6885405.

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Mahboob, Amir Soltany, and Mohammad Reza Ostadi Moghaddam. "A Neuro-Fuzzy Classifier Based on Evolutionary Algorithms." In 2021 26th International Computer Conference, Computer Society of Iran (CSICC). IEEE, 2021. http://dx.doi.org/10.1109/csicc52343.2021.9420556.

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Haghighat, A. T., M. Esmaeili, A. Saremi, and V. R. Mousavi. "Intrusion Detection via Fuzzy-Genetic Algorithm Combination with Evolutionary Algorithms." In 2007 International Conference on Computer and Information Science. IEEE, 2007. http://dx.doi.org/10.1109/icis.2007.124.

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Anton, Mihai. "Automated Machine Learning using Evolutionary Algorithms." In 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2020. http://dx.doi.org/10.1109/iccp51029.2020.9266163.

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Zhang Kejun and Sun Shouqian. "Music emotional design by evolutionary algorithms." In 2010 IEEE 11th International Conference on Computer-Aided Industrial Design & Conceptual Design 1. IEEE, 2010. http://dx.doi.org/10.1109/caidcd.2010.5681231.

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Ghaffarizadeh, Ahmadreza, Kamelia Ahmadi, and Mehdi Eftekhari. "Adding Crossover to Extinction-Based Evolutionary Algorithms." In 2009 Second International Conference on Computer and Electrical Engineering. IEEE, 2009. http://dx.doi.org/10.1109/iccee.2009.125.

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Lugo, Anthony Erb, Dennis Garcia, Erik Hemberg, and Una-May O'Reilly. "Developing proactive defenses for computer networks with coevolutionary genetic algorithms." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3067695.3089234.

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Савостин, Игорь, Igor' Savostin, Андрей Трубаков, and Andrey Trubakov. "The Combination of Morphological and Evolutionary Algorithms for Graph-based Pathfinding on Maps with Complex Topologies." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-300-303.

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One of the difficult problems to solve has always been and still remains the problem of finding a path either in a graphic chart or a graphic maze of large size. The main problem is that traditional algorithms require a lot of time due to combinatorial complexity. At the same time, both classical algorithms based on the search of variants (such as Dijkstra's algorithm, A*, ARA*, D* lite), and stochastic algorithms (ant algorithm, genetic), alongside with algorithms based on morphology (wave) are not always able to achieve the goal. The article proposes a new modification of the path-finding al
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McGerty, Sean, and Frank Moisiadis. "Are Evolutionary Algorithms Required to Solve Sudoku Problems?" In Fourth International conference on Computer Science & Information Technology. Academy & Industry Research Collaboration Center (AIRCC), 2014. http://dx.doi.org/10.5121/csit.2014.4231.

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Simu, Shreyas, and Mayuri Naik. "Dental X-ray Image Enhancement using Evolutionary Algorithms." In 2020 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2020. http://dx.doi.org/10.1109/iccci48352.2020.9104153.

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