Relevant bibliographies by topics / Evolutionary programming (Computer science) Mathematical optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Evolutionary programming (Computer science) Mathematical optimization'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Evolutionary programming (Computer science) Mathematical optimization"

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Jong-Hwan Kim and Hyun Myung. "Evolutionary programming techniques for constrained optimization problems." IEEE Transactions on Evolutionary Computation 1, no. 2 (July 1997): 129–40. http://dx.doi.org/10.1109/4235.687880.

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Abo-Elnaga, Yousria, and Sarah Nasr. "Modified Evolutionary Algorithm and Chaotic Search for Bilevel Programming Problems." Symmetry 12, no. 5 (May 6, 2020): 767. http://dx.doi.org/10.3390/sym12050767.

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Bi-level programming problem (BLPP) is an optimization problem consists of two interconnected hierarchical optimization problems. Solving BLPP is one of the hardest tasks facing the optimization community. This paper proposes a modified genetic algorithm and a chaotic search to solve BLPP. Firstly, the proposed algorithm solves the upper-level problem using a modified genetic algorithm. The genetic algorithm has modified with a new selection technique. The new selection technique helps the upper-level decision-maker to take an appropriate decision in anticipation of a lower level’s reaction. It distinguishes the proposed algorithm with a very small number of solving the lower-level problem, enhances the algorithm performance and fasts convergence to the solution. Secondly, a local search based on chaos theory has applied around the modified genetic algorithm solution. Chaotic local search enables the algorithm to escape from local solutions and increase convergence to the global solution. The proposed algorithm has evaluated on forty different test problems to show the proposed algorithm effectiveness. The results have analyzed to illustrate the new selection technique effect and the chaotic search effect on the algorithm performance. A comparison between the proposed algorithm results and other state-of-the-art algorithms results has introduced to show the proposed algorithm superiority.
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IWAMATSU, MASAO. "COMPARISON OF PARTICLE SWARM AND EVOLUTIONARY PROGRAMMING AS THE GLOBAL CONFORMATION OPTIMIZER OF CLUSTERS." International Journal of Modern Physics C 16, no. 04 (April 2005): 591–606. http://dx.doi.org/10.1142/s0129183105007340.

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The particle swarm optimization (PSO) algorithm and two variants of the evolutionary programming (EP) are applied to the several function optimization problems and the conformation optimization of atomic clusters to check the performance of these algorithms as a general-purpose optimizer. It was found that the PSO is superior to the EP though the PSO is not equipped with the mechanism of self-adaptation of search strategies of the EP. The PSO cannot find the global minimum for the atomic cluster but can find it for similar multi-modal benchmark functions of the same size. The size of the cluster which can be handled by the PSO and the EP is limited, and is similar to the one amenable to the popular simulated annealing. The result for benchmark functions only serves as an indication of the performance of the algorithm.
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Nurcahyadi, Teddy, and Christian Blum. "Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study." Mathematics 9, no. 4 (February 11, 2021): 361. http://dx.doi.org/10.3390/math9040361.

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Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. This is also the case in other learning-based metaheuristics such as evolutionary algorithms and particle swarm optimization. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this work we present and study an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. Moreover, we compare our proposal to some well-known existing negative learning approaches from the related literature. Our study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases we are able to show that our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.
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Regis, Rommel G. "Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization Using Radial Basis Functions." IEEE Transactions on Evolutionary Computation 18, no. 3 (June 2014): 326–47. http://dx.doi.org/10.1109/tevc.2013.2262111.

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Sato, Mayuko, Yoshikazu Fukuyama, Tatsuya Iizaka, and Tetsuro Matsui. "Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration." Algorithms 12, no. 1 (January 7, 2019): 15. http://dx.doi.org/10.3390/a12010015.

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This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO2 emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO2 emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters.
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Li, Hecheng, and Lei Fang. "Co-evolutionary algorithm: An efficient approach for bilevel programming problems." Engineering Optimization 46, no. 3 (April 29, 2013): 361–76. http://dx.doi.org/10.1080/0305215x.2013.772601.

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Chen, Yi, Aimin Zhou, and Swagatam Das. "Utilizing dependence among variables in evolutionary algorithms for mixed-integer programming: A case study on multi-objective constrained portfolio optimization." Swarm and Evolutionary Computation 66 (October 2021): 100928. http://dx.doi.org/10.1016/j.swevo.2021.100928.

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Sutton, Andrew M., Frank Neumann, and Samadhi Nallaperuma. "Parameterized Runtime Analyses of Evolutionary Algorithms for the Planar Euclidean Traveling Salesperson Problem." Evolutionary Computation 22, no. 4 (December 2014): 595–628. http://dx.doi.org/10.1162/evco_a_00119.

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Parameterized runtime analysis seeks to understand the influence of problem structure on algorithmic runtime. In this paper, we contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the Euclidean traveling salesperson problem (Euclidean TSP). We investigate the structural properties in TSP instances that influence the optimization process of evolutionary algorithms and use this information to bound their runtime. We analyze the runtime in dependence of the number of inner points k. In the first part of the paper, we study a [Formula: see text] EA in a strictly black box setting and show that it can solve the Euclidean TSP in expected time [Formula: see text] where A is a function of the minimum angle [Formula: see text] between any three points. Based on insights provided by the analysis, we improve this upper bound by introducing a mixed mutation strategy that incorporates both 2-opt moves and permutation jumps. This strategy improves the upper bound to [Formula: see text]. In the second part of the paper, we use the information gained in the analysis to incorporate domain knowledge to design two fixed-parameter tractable (FPT) evolutionary algorithms for the planar Euclidean TSP. We first develop a [Formula: see text] EA based on an analysis by M. Theile, 2009, ”Exact solutions to the traveling salesperson problem by a population-based evolutionary algorithm,” Lecture notes in computer science, Vol. 5482 (pp. 145–155), that solves the TSP with k inner points in [Formula: see text] generations with probability [Formula: see text]. We then design a [Formula: see text] EA that incorporates a dynamic programming step into the fitness evaluation. We prove that a variant of this evolutionary algorithm using 2-opt mutation solves the problem after [Formula: see text] steps in expectation with a cost of [Formula: see text] for each fitness evaluation.
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Urselmann, Maren, Michael T. M. Emmerich, Jochen Till, Guido Sand, and Sebastian Engell. "Design of problem-specific evolutionary algorithm/mixed-integer programming hybrids: two-stage stochastic integer programming applied to chemical batch scheduling." Engineering Optimization 39, no. 5 (July 2007): 529–49. http://dx.doi.org/10.1080/03052150701364659.

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Dissertations / Theses on the topic "Evolutionary programming (Computer science) Mathematical optimization"

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Service, Travis. "Co-optimization: a generalization of coevolution." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2008. http://scholarsmine.mst.edu/thesis/pdf/Service_09007dcc804e2264.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 April 26, 2008) Includes bibliographical references (p. 65-68).
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Gadiraju, Sriphani Raju. "Modified selection mechanisms designed to help evolution strategies cope with noisy response surfaces." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-07022003-164112.

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Muthuswamy, Shanthi. "Discrete particle swarm optimization algorithms for orienteering and team orienteering problems." Diss., Online access via UMI:, 2009.

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Khan, Salman A. "Design and analysis of evolutionary and swarm intelligence techniques for topology design of distributed local area networks." Pretori: [S.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-09272009-153908/.

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Wong, Yin-cheung Eugene, and 黃彥璋. "A hybrid evolutionary algorithm for optimization of maritime logisticsoperations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44526763.

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Doddapaneni, Srinivas P. "Automatic dynamic decomposition of programs on distributed memory machines." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/8158.

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Abdel, Raheem Mohamed. "Electimize a new evolutionary algorithm for optimization with applications in construction engineering." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4833.

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Optimization is considered an essential step in reinforcing the efficiency of performance and economic feasibility of construction projects. In the past few decades, evolutionary algorithms (EAs) have been widely utilized to solve various types of construction-related optimization problems due to their efficiency in finding good solutions in relatively short time periods. However, in many cases, these existing evolutionary algorithms failed to identify the optimal solution to several optimization problems. As such, it is deemed necessary to develop new approaches in order to help identify better-quality solutions. This doctoral research presents the development of a new evolutionary algorithm, named "Electimize," that is based on the simulation of the flow of electric current in the branches of an electric circuit. The main motive in this research is to provide the construction industry with a robust optimization tool that overcomes some of the shortcomings of existing EAs. In solving optimization problems using Electimize, a number of wires (solution strings) composed of a number of segments are fabricated randomly. Each segment corresponds to a decision variable in the objective function. The wires are virtually connected in parallel to a source of an electricity to represent an electric circuit. The electric current passing through each wire is calculated by substituting the values of the segments in the objective function. The quality of the wire is based on its global resistance, which is calculated using Ohm's law.; The first problem is the cash flow management problem, as mentioned earlier. The second problem is the time cost tradeoff problem (TCTP) and is used as an example of static optimization. The third problem is a site layout planning problem (SLPP), and represents dynamic optimization. When Electimize was applied to the TCTP, it succeeded to identify the optimal solution of the problem in a single iteration using thirty solution strings, compared to hundreds of iterations and solution strings that were used by EAs to solve the same problem. Electimize was also successful in solving the SLPP and outperformed the existing algorithm used to solve the problem by identifying a better optimal solution. The main contributions of this research are 1) developing a new approach and algorithm for optimization based on the simulation of the phenomenon of electrical conduction, 2) devising processes that enable assessing the quality of decision variable values independently, 3) formulating methodologies that allow for the extensive search of the solution space and identification of alternative optimal solutions, and 4) providing a robust optimization tool for decision makers and construction planners.; The main objectives of this research are to 1) develop an optimization methodology that is capable of evaluating the quality of decision variable values in the solution string independently; 2) devise internal optimization mechanisms that would enable the algorithm to extensively search the solution space and avoid its convergence toward local optima; and 3) provide the construction industry with a reliable optimization tool that is capable of solving different classes of NP-hard optimization problems. First, internal processes are designed, modeled, and tested to enable the individual assessment of the quality of each decision variable value available in the solution space. The main principle in assessing the quality of each decision variable value individually is to use the segment resistance (local resistance) as an indicator of the quality. This is accomplished by conducting a sensitivity analysis to record the change in the resistance of a control wire, when a certain decision variable value is substituted into the corresponding segment of the control wire. The calculated local resistances of all segments of a wire are then normalized to ensure that their summation is equal to the global wire resistance and no violation is made of Kirchhoff's rule. A benchmark NP-hard cash flow management problem from the literature is attempted to test and validate the performance of the developed approach. Not only was Electimize able to identify the optimal solution for the problem, but also it identified ten alternative optimal solutions, outperforming the existing algorithms. Second, the internal processes for the sensitivity analysis are designed to allow for extensive search of the solution space through the generation of new wires. Every time a decision variable value is substituted in the control wire to assess its quality, a new wire that might have a better quality is generated.; To further test the capabilities of Electimize in searching the solution space, Electimize was applied to a multimodal 9-city travelling salesman problem (TSP) that had been previously designed and solved mathematically. The problem has 27 alternative optimal solutions. Electimize succeeded to identify 21 of the 27 alternative optimal solutions in a limited time period. Moreover, Electimize was applied to a 16-city benchmark TSP (Ulysses16) and was able to identify the optimal tour and its alternative. Further, additional parameters are incorporated to 1) allow for the extensive search of the solution space, 2) prevent the convergence towards local optima, and 3) increase the rate of convergence towards the global optima. These parameters are classified into two categories: 1) resistance related parameters, and 2) solution exploration parameters. The resistance related parameters are: a) the conductor resistivity, b) its cross-sectional area, and c) the length of each segment. The main role of this set of parameters is to provide the algorithm with additional gauging parameters to help guide it towards the global optima. The solution exploration parameters included a) the heat factor, and b) the criterion of selecting the control wire. The main role of this set of parameters is to allow for an extensive search of the solution space in order to facilitate the identification all the available alternative optimal solutions; prevent the premature convergence towards local optima; and increase the rate of convergence towards the global optima. Two TSP instances (Bayg29 and ATT48) are attempted and the results obtained illustrate that Electimize outperforms other EAs with respect to the quality of solutions obtained. Third, to test the capabilities of Electimize as a reliable optimization tool in construction optimization problems, three benchmark NP-hard construction optimization problems are attempted.
ID: 030422839; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (Ph.D.)--University of Central Florida, 2011.; Includes bibliographical references (p. 263-268).
Ph.D.
Doctorate
Civil, Environmental and Construction Engineering
Engineering and Computer Science
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Garret, Aaron Dozier Gerry V. "Neural enhancement for multiobjective optimization." Auburn, Ala., 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Computer_Science_and_Software_Engineering/Dissertation/Garrett_Aaron_55.pdf.

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Ngatchou, Patrick. "Intelligent techniques for optimization and estimation /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/5827.

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Rohling, Gregory Allen. "Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objectives." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4835.

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This research augments current Multiple Objective Evolutionary Algorithms with methods that dramatically reduce the time required to evolve toward a region of interest in objective space. Multiple Objective Evolutionary Algorithms (MOEAs) are superior to other optimization techniques when the search space is of high dimension and contains many local minima and maxima. Likewise, MOEAs are most interesting when applied to non-intuitive complex systems. But, these systems are often computationally expensive to calculate. When these systems require independent computations to evaluate each objective, the computational expense grows with each additional objective. This method has developed methods that reduces the time required for evolution by reducing the number of objective evaluations, while still evolving solutions that are Pareto optimal. To date, all other Multiple Objective Evolutionary Algorithms (MOEAs) require the evaluation of all objectives before a fitness value can be assigned to an individual. The original contributions of this thesis are: 1. Development of a hierarchical search space description that allows association of crossover and mutation settings with elements of the genotypic description. 2. Development of a method for parallel evaluation of individuals that removes the need for delays for synchronization. 3. Dynamical evolution of thresholds for objectives to allow partial evaluation of objectives for individuals. 4. Dynamic objective orderings to minimize the time required for unnecessary objective evaluations. 5. Application of MOEAs to the computationally expensive flare pattern design domain. 6. Application of MOEAs to the optimization of fielded missile warning receiver algorithms. 7. Development of a new method of using MOEAs for automatic design of pattern recognition systems.
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Books on the topic "Evolutionary programming (Computer science) Mathematical optimization"

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Branke, Jürgen. Evolutionary optimization in dynamic environments. Boston: Kluwer Academic Publishers, 2002.

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Fred, Glover, and Dorigo Marco, eds. New ideas in optimization. London: McGraw-Hill, 1999.

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M, Keijzer, and Association for Computing Machinery. SIGEVO., eds. GECCO 2006: Genetic and Evolutionary Computation COnference : July 8-12, 2006, Seattle, Washington, USA. New York, NY: Association for Computing Machinery, 2006.

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Design by evolution: Advances in evolutionary design. Berlin: Springer, 2008.

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Laguna, Manuel. Scatter search: Methodology and implementation in C. Boston, MA: Kluwer Academic Publishers, 2003.

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Rafael, Martí, ed. Scatter search: Methodology and implementations in C. Boston: Kluwer Academic Publishers, 2003.

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A, Hashem M. M., ed. Evolutionary computations: New algorithms and their applications to evolutionary robots. Berlin: Springer-Verlag, 2004.

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Middendorf, Martin. Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Branke, Jürgen. Evolutionary Optimization in Dynamic Environments. Boston, MA: Springer US, 2002.

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Integrated methods for optimization. New York: Springer, 2012.

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Book chapters on the topic "Evolutionary programming (Computer science) Mathematical optimization"

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Myung, Hyun, and Jong-Hwan Kim. "Lagrangian-based evolutionary programming for constrained optimization." In Lecture Notes in Computer Science, 35–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0028519.

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Kumar, Chellapilla, Sathyanarayan S. Rao, and Ahmad Hoorfar. "Optimization of thinned phased arrays using evolutionary programming." In Lecture Notes in Computer Science, 157–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0040769.

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He, Jingsong, Zhengyu Yang, and Xin Yao. "Hybridisation of Particle Swarm Optimization and Fast Evolutionary Programming." In Lecture Notes in Computer Science, 392–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11903697_50.

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Chung, ChanJin, and Robert G. Reynolds. "Function optimization using evolutionary programming with self-adaptive cultural algorithms." In Lecture Notes in Computer Science, 17–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0028517.

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Song, Won-Kyung, and Zeungnam Bien. "Optimization of parameters of color image segmentation using evolutionary programming." In Lecture Notes in Computer Science, 97–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0028526.

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Pinheiro, Plácido Rogério, and Amaury Brasil Filho. "Web-Based Optimization System Applied to the Teaching of Mathematical Programming." In Communications in Computer and Information Science, 667–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13166-0_93.

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Luo, Yuanfei, Jiehui Tang, Si Xu, Li Zhu, and Xiang Li. "Application and Research of Shortest Time Limit-Resource Leveling Optimization Problem Based on a New Modified Evolutionary Programming." In Communications in Computer and Information Science, 587–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34289-9_65.

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dos Santos, André G., Rodolfo P. Araujo, and José E. C. Arroyo. "A Combination of Evolutionary Algorithm, Mathematical Programming, and a New Local Search Procedure for the Just-In-Time Job-Shop Scheduling Problem." In Lecture Notes in Computer Science, 10–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13800-3_2.

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Conference papers on the topic "Evolutionary programming (Computer science) Mathematical optimization"

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Ismail, Mohamed A., Attia H. Gomaa, and Ashraf O. Nassef. "Solving the Multi-Objective Facility Layout Problem Using Evolutionary Multi-Objective Optimization Algorithms." In ASME 2006 International Manufacturing Science and Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/msec2006-21067.

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The multi-objective facility layout problem is defined in the literature as an extension of the famous quadratic assignment problem (QAP). Most previous mathematical models tried to combine both the quantitative and the qualitative objectives into a single objective by using weighting factors. This paper introduces a multi-objective mathematical model and solves it using the revised Strength Pareto Evolutionary Algorithm (SPEAII). The purpose of this paper is to find an efficient set of solutions “Pareto optimal set” which could be introduced to the decision maker to select the best alternative, while considering conflicting and noncommensurate objectives. A computer program is developed to define the mathematical model, code candidate solutions into genetic form, and use Evolutionary Multi-Objective Optimization algorithms (EMO) to find the efficient set of solutions. The problem model is built according to its customized data input. The suggested model and solution algorithms are applied to a wide set of different benchmark problems. Results showed the superiority of the suggested models and algorithms in terms of the quality of solution and objective space exploration.
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Guirguis, David, David A. Romero, and Cristina H. Amon. "Efficient Wind Turbine Micrositing in Large-Scale Wind Farms." In ASME 2016 10th International Conference on Energy Sustainability collocated with the ASME 2016 Power Conference and the ASME 2016 14th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/es2016-59594.

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As wind energy is established as a sustainable alternative source of electricity, very large-scale wind farms with hundreds of turbines are becoming increasingly common. For the optimal design of wind farm layouts, the number of decision variables is at least twice the number of turbines (e.g., the Cartesian coordinates of each turbine). As the number of turbines increases, the computational cost incurred by the optimization solver to converge to a satisfactory solution increases as well. This issue represents a serious limitation in the computer-aided design of large wind farms. Moreover, the wind farm domains are typically highly constrained including land-availability and proximity constraints. These non-linear constraints increase the complexity of the optimization problem and decrease the likelihood of obtaining even a feasible solution. Several approaches have been proposed for micrositing of wind turbines, including random searches, mixed-integer programs, and metaheuristics. Each of these methods has its own trade-off between the quality of optimized layouts and the computational cost of obtaining the solution. In this paper, we demonstrate the capability of non-linear mathematical programming for optimizing very large-scale wind farms by leveraging explicit, analytical derivatives for the objective and constraint functions, thus overcoming the aforementioned limitations while also providing convergence and local optimality guarantees. For that purpose, two large farms with hundreds of turbines and significant land-use constraints are solved on a standard personal computer.
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