Relevant bibliographies by topics / Evolutionary algorithms (EA)

Academic literature on the topic 'Evolutionary algorithms (EA)'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Evolutionary algorithms (EA)"

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Elhossini, Ahmed, Shawki Areibi, and Robert Dony. "Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization." Evolutionary Computation 18, no. 1 (March 2010): 127–56. http://dx.doi.org/10.1162/evco.2010.18.1.18105.

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This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.
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Czajkowski, Marcin, and Marek Kretowski. "Evolutionary Approach for Relative Gene Expression Algorithms." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/593503.

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A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space.
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Borisovsky, P. A., and A. V. Eremeev. "Comparing evolutionary algorithms to the (1+1) -EA." Theoretical Computer Science 403, no. 1 (August 2008): 33–41. http://dx.doi.org/10.1016/j.tcs.2008.03.008.

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Sekanina, Lukas. "Evolutionary Algorithms in Approximate Computing: A Survey." Journal of Integrated Circuits and Systems 16, no. 2 (August 16, 2021): 1–12. http://dx.doi.org/10.29292/jics.v16i2.499.

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In recent years, many design automation methods have been developed to routinely create approximate implementations of circuits and programs that show excellent trade-offs between the quality of output and required resources. This paper deals with evolutionary approximation as one of the popular approximation methods. The paper provides the first survey of evolutionary algorithm (EA)-based approaches applied in the context of approximate computing. The survey reveals that EAs are primarily applied as multi-objective optimizers. We propose to divide these approaches into two main classes: (i) parameter optimization in which the EA optimizes a vector of system parameters, and (ii) synthesis and optimization in which EA is responsible for determining the architecture and parameters of the resulting system. The evolutionary approximation has been applied at all levels of design abstraction and in many different applications. The neural architecture search enabling the automated hardware-aware design of approximate deep neural networks was identified as a newly emerging topic in this area.
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Catania, Carlos Adrian, Cecilia Zanni-Merk, François de Bertrand de Beuvron, and Pierre Collet. "Ontologies to Lead Knowledge Intensive Evolutionary Algorithms." International Journal of Knowledge and Systems Science 7, no. 1 (January 2016): 78–100. http://dx.doi.org/10.4018/ijkss.2016010105.

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Evolutionary Algorithms (EA) have proven to be very effective in optimizing intractable problems in many areas. However, real problems including specific constraints are often overlooked by the proposed generic models. The authors' goal here is to show how knowledge engineering techniques can be used to guide the definition of Evolutionary Algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. They propose a methodology based on the structuring of the conceptual model underlying the problem, in the form of a labelled domain ontology suitable for optimization by EA. The case studyfocuses on the logistics involved in the transportation of patients. Although this problem belongs to the well-known family of Vehicle Routing Problems, its specificity comes from the data and constraints (cost, legal and health considerations) that must be taken into account. The precise definition of the knowledge model with thelabelled domain ontology permits the formal description of the chromosome, the fitness functions and the genetic operators.
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REICHELT, DIRK, and FRANZ ROTHLAUF. "RELIABLE COMMUNICATION NETWORK DESIGN WITH EVOLUTIONARY ALGORITHMS." International Journal of Computational Intelligence and Applications 05, no. 02 (June 2005): 251–66. http://dx.doi.org/10.1142/s146902680500160x.

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For the reliable communication network design (RCND) problem unreliable links are available, each bearing several options which have different levels of reliability and varying costs. The goal is to find the most cost-effective communication network design that satisfies a predefined overall reliability constraint. This paper presents two new evolutionary algorithm (EA) approaches to solving the RCND problem: LaBORNet and BaBORNet. LaBORNet uses an encoding that represents the network topology as well as the used link options while repairing infeasible solutions using an additional repair heuristic (CURE). BaBORNet encodes only the network topology and determines the link options by using the repair heuristic CURE as a local search method. The experimental results show that the new EA approaches using repair heuristics outperform existing EA approaches from the literature using penalties for infeasible solutions. They also find better solutions for existing problems from the literature, as well as for new and larger test problems.
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Krenich, Stanisław. "Parallel Evolutionary Algorithm for Computationally Expensive Single Criteria Design Optimization." Applied Mechanics and Materials 555 (June 2014): 586–92. http://dx.doi.org/10.4028/www.scientific.net/amm.555.586.

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The paper presents an approach to design optimization using parallel evolutionary algorithms. The only use of a simple evolutionary algorithm in order to generate the optimal solution for complex problems can be ineffective due to long calculation time. Thus a tournament evolutionary algorithm (EA) and a parallel computation method are proposed and used. The proposed EA does not require an analysis of the optimization model for each potential solution from evolutionary populations. The second element of the method consists in parallel running of evolutionary algorithms using multi-threads approach. The experiments were carried out for many different single design optimization problems and two of them are presented in the paper. The first problem considers a task of robot gripper mechanism optimization and the second one deals with the optimization of a shaft based on Finite Element Method analysis. From the generated results it is clear that proposed approach is a very effective tool for solving fairly complicated tasks considering both the accuracy and the time of calculation.
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Ter-Sarkisov, Aram, and Stephen Marsland. "Convergence Properties of (μ + λ) Evolutionary Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1816–17. http://dx.doi.org/10.1609/aaai.v25i1.8037.

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We present a number of convergence properties of population-based Evolutionary Algorithms (EAs) on a set of test functions. Focus is on EA using k-Bit-Swap (kBS) operator. We compare our findings to past research.
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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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Sung, Chi Wan, and Shiu Yin Yuen. "Analysis of (1+1) Evolutionary Algorithm and Randomized Local Search with Memory." Evolutionary Computation 19, no. 2 (June 2011): 287–323. http://dx.doi.org/10.1162/evco_a_00029.

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This paper considers the scenario of the (1+1) evolutionary algorithm (EA) and randomized local search (RLS) with memory. Previously explored solutions are stored in memory until an improvement in fitness is obtained; then the stored information is discarded. This results in two new algorithms: (1+1) EA-m (with a raw list and hash table option) and RLS-m+ (and RLS-m if the function is a priori known to be unimodal). These two algorithms can be regarded as very simple forms of tabu search. Rigorous theoretical analysis of the expected time to find the globally optimal solutions for these algorithms is conducted for both unimodal and multimodal functions. A unified mathematical framework, involving the new concept of spatially invariant neighborhood, is proposed. Under this framework, both (1+1) EA with standard uniform mutation and RLS can be considered as particular instances and in the most general cases, all functions can be considered to be unimodal. Under this framework, it is found that for unimodal functions, the improvement by memory assistance is always positive but at most by one half. For multimodal functions, the improvement is significant; for functions with gaps and another hard function, the order of growth is reduced; for at least one example function, the order can change from exponential to polynomial. Empirical results, with a reasonable fitness evaluation time assumption, verify that (1+1) EA-m and RLS-m+ are superior to their conventional counterparts. Both new algorithms are promising for use in a memetic algorithm. In particular, RLS-m+ makes the previously impractical RLS practical, and surprisingly, does not require any extra memory in actual implementation.
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Dissertations / Theses on the topic "Evolutionary algorithms (EA)"

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Zini, Érico de Oliveira Costa [UNESP]. "Algoritmo genético especializado na resolução de problemas com variáveis contínuas e altamente restritos." Universidade Estadual Paulista (UNESP), 2009. http://hdl.handle.net/11449/87116.

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Made available in DSpace on 2014-06-11T19:22:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-02-20Bitstream added on 2014-06-13T19:28:05Z : No. of bitstreams: 1 zini_eoc_me_ilha.pdf: 1142984 bytes, checksum: 4ff93a7fe459a5a56e15da26b7a6dd45 (MD5)
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Este trabalho apresenta uma metodologia composta de duas fases para resolver problemas de otimização com restrições usando uma estratégia multiobjetivo. Na primeira fase, o esforço concentra-se em encontrar, pelo menos, uma solução factível, descartando completamente a função objetivo. Na segunda fase, aborda-se o problema como biobjetivo, onde se busca a otimização da função objetivo original e maximizar o cumprimento das restrições. Na fase um propõe-se uma estratégia baseada na diminuição progressiva da tolerância de aceitação das restrições complexas para encontrar soluções factíveis. O desempenho do algoritmo é validado através de 11 casos testes bastantes conhecidos na literatura especializada.
This work presents a two-phase framework for solving constrained optimization problems using a multi-objective strategy. In the first phase, the objective function is completely disregarded and entire search effort is directed toward finding a single feasible solution. In the second phase, the problem is treated as a bi-objective optimization problem, where the technique converts constrained optimization to a two-objective optimization: one is the original objective function; the other is the degree function violating the constraints. In the first phase a methodology based on progressive decrease of the tolerance of acceptance of complex constrains is proposed in order to find feasible solutions. The approach is tested on 11 well-know benchmark functions.
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Huang, Zhengwen. "Schema theory for gene expression programming." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/8539.

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This thesis studied a new variant of Evolutionary Algorithms called Gene Expression Programming. The evolution process of Gene Expression Programming was investigated from the practice to the theory. As a practice level, the original version of Gene Expression Programming was applied to a classification problem and an enhanced version of the algorithm was consequently developed. This allowed the development of a general understanding of each component of the genotype and phenotype separated representation system of the solution employed by the algorithm. Based on such an understanding, a version of the schema theory was developed for Gene Expression Programming. The genetic modifications provided by each genetic operator employed by this algorithm were analysed and a set of theorems predicting the propagation of the schema from one generation to another was developed. Also a set of experiments were performed to test the validity of the developed schema theory obtaining good agreement between the experimental results and the theoretical predictions.
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Zini, Érico de Oliveira Costa. "Algoritmo genético especializado na resolução de problemas com variáveis contínuas e altamente restritos /." Ilha Solteira : [s.n.], 2009. http://hdl.handle.net/11449/87116.

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Resumo: Este trabalho apresenta uma metodologia composta de duas fases para resolver problemas de otimização com restrições usando uma estratégia multiobjetivo. Na primeira fase, o esforço concentra-se em encontrar, pelo menos, uma solução factível, descartando completamente a função objetivo. Na segunda fase, aborda-se o problema como biobjetivo, onde se busca a otimização da função objetivo original e maximizar o cumprimento das restrições. Na fase um propõe-se uma estratégia baseada na diminuição progressiva da tolerância de aceitação das restrições complexas para encontrar soluções factíveis. O desempenho do algoritmo é validado através de 11 casos testes bastantes conhecidos na literatura especializada.
Abstract: This work presents a two-phase framework for solving constrained optimization problems using a multi-objective strategy. In the first phase, the objective function is completely disregarded and entire search effort is directed toward finding a single feasible solution. In the second phase, the problem is treated as a bi-objective optimization problem, where the technique converts constrained optimization to a two-objective optimization: one is the original objective function; the other is the degree function violating the constraints. In the first phase a methodology based on progressive decrease of the tolerance of acceptance of complex constrains is proposed in order to find feasible solutions. The approach is tested on 11 well-know benchmark functions.
Orientador: Rubén Augusto Romero Lázaro
Coorientador: José Roberto Sanches Mantovani
Banca: Antonio Padilha Feltrin
Banca: Marcos Julio Rider Flores
Mestre
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Pospíchal, Petr. "Akcelerace genetického algoritmu s využitím GPU." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236783.

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This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. First chapter deeply analyses Genetic algorithms and corresponding topics like population, chromosome, crossover, mutation and selection. Next part of the thesis shows GPU abilities for unified computing using both DirectX/OpenGL with Cg and specialized GPGPU libraries like CUDA. The fourth chapter focuses on design of GPU implementation using CUDA, coarse-grained and fine-grained GAs are discussed, and completed by sorting and random number generation task accelerated by GPU. Next chapter covers implementation details -- migration, crossover and selection schemes mapped on CUDA software model. All GA elements and quality of GPU results are described in the last chapter.
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Thomas, George L. "Biogeography-Based Optimization of a Variable Camshaft Timing System." Cleveland State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=csu1419775790.

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Bazargani, Mosab. "Affine image registration using genetic algorithms and evolutionary strategies." Master's thesis, 2012. http://hdl.handle.net/10400.1/10891.

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This thesis investigates the application of evolutionary algorithms to align two or more 2-D images by means of image registration. The proposed search strategy is a transformation parameters-based approach involving the affine transform. A noisy objective function is proposed and tested using two well-known evolutionary algorithms (EAs), the genetic algorithm (GA) as well as the evolutionary strategies (ES) that are suitable for this particular ill-posed problem. In contrast with GA, which was originally designed to work on binary representation, ES was originally developed to work in continuous search spaces. Surprisingly, results of the proposed real coded genetic algorithm are far superior when compared to results obtained from evolutionary strategies’ framework for the problem at hand. The real coded GA uses Simulated Binary Crossover (SBX), a parent-centric recombination operator that has shown to deliver a good performance in many optimization problems in the continuous domain. In addition, a new technique for matching points, between a warped and static images by using a randomized ordering when visiting the points during the matching procedure, is proposed. This new technique makes the evaluation of the objective function somewhat noisy, but GAs and other population-based search algorithms have been shown to cope well with noisy fitness evaluations. The results obtained from GA formulation are competitive to those obtained by the state-of-the-art classical methods in image registration, confirming the usefulness of the proposed noisy objective function and the suitability of SBX as a recombination operator for this type of problem.
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Books on the topic "Evolutionary algorithms (EA)"

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Pierre, Liardet, ed. Artificial evolution: 6th International Conference, Evolution Artificielle, EA 2003, Marseille, France, October 27-30, 2003 ; revised selected papers. Berlin: Springer, 2004.

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1965-, Talbi El-Ghazali, ed. Artificial evolution: 7th international conference, Evolution Artificielle, EA 2005, Lille, France, October 26-28, 2005 : revised selected papers. Berlin: Springer, 2006.

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Nicolas, Monmarché, ed. Artificial evolution: 8th international conference, Evolution Artificielle, EA 2007 : Tours, France, October 29-31, 2007 : revised selected papers. Berlin: Springer, 2008.

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EA 2009 (2009 Strasbourg, France). Artificial evolution: 9th international conference, evolution artificielle, EA 2009, Strasbourg, France, October 26-28, 2009 : revised selected papers. Berlin: Springer, 2010.

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Talbi, El-Ghazali, Pierre Collet, Evelyne Lutton, Marc Schoenauer, and Pierre Liardet. Artificial Evolution: 7th International Conference, Evolution Artificielle, EA 2005, Revised Selected Papers. Springer London, Limited, 2006.

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Collet, Pierre, Nicolas Monmarché, Jin-Kao Hao, Evelyne Lutton, Pierrick Legrand, and Marc Schoenauer. Artificial Evolution: 10th International Conference, Evolution Artificielle, EA 2011, Angers, France, October 24-26, 2011, Revised Selected Papers. Springer, 2012.

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Monmarché, Nicolas, Evelyne Lutton, Pierrick Legrand, Pierre Parrend, and Marc Schoenauer. Artificial Evolution: 13th International Conference, Évolution Artificielle, EA 2017, Paris, France, October 25–27, 2017, Revised Selected Papers. Springer, 2018.

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Lutton, Evelyne, Pierrick Legrand, Marc Schoenauer, Stephane Bonnevay, and Nicolas Monmarche. Artificial Evolution: 12th International Conference, Evolution Artificielle, EA 2015, Lyon, France, October 26-28, 2015. Revised Selected Papers. Springer, 2016.

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Monmarché, Nicolas, Jin-Kao Hao, Evelyne Lutton, Pierrick Legrand, and Marc-Michel Corsini. Artificial Evolution: 11th International Conference, Evolution Artificielle, EA 2013, Bordeaux, France, October 21-23, 2013. Revised Selected Papers. Springer London, Limited, 2014.

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Monmarché, Nicolas, Evelyne Lutton, Pierrick Legrand, Marc Schoenauer, Lhassane Idoumghar, and Arnaud Liefooghe. Artificial Evolution: 14th International Conference, Évolution Artificielle, EA 2019, Mulhouse, France, October 29–30, 2019, Revised Selected Papers. Springer, 2020.

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Book chapters on the topic "Evolutionary algorithms (EA)"

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Morrison, Ronald W. "A New EA for Dynamic Problems." In Designing Evolutionary Algorithms for Dynamic Environments, 53–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-06560-0_5.

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Nissen, Volker. "Evolutionäre Algorithmen (EA)." In Evolutionäre Algorithmen, 13–196. Wiesbaden: Deutscher Universitätsverlag, 1994. http://dx.doi.org/10.1007/978-3-322-83430-0_3.

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Nissen, Volker. "EA nah verwandte Optimierungsmethoden." In Einführung in Evolutionäre Algorithmen, 217–36. Wiesbaden: Vieweg+Teubner Verlag, 1997. http://dx.doi.org/10.1007/978-3-322-93861-9_6.

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Nissen, Volker. "Vergleich und Beurteilung von EA." In Einführung in Evolutionäre Algorithmen, 237–64. Wiesbaden: Vieweg+Teubner Verlag, 1997. http://dx.doi.org/10.1007/978-3-322-93861-9_7.

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Michalak, Krzysztof. "Sim-EA: An Evolutionary Algorithm Based on Problem Similarity." In Intelligent Data Engineering and Automated Learning – IDEAL 2014, 191–98. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10840-7_24.

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Michalak, Krzysztof. "The Sim-EA Algorithm with Operator Autoadaptation for the Multiobjective Firefighter Problem." In Evolutionary Computation in Combinatorial Optimization, 184–96. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16468-7_16.

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Dilettoso, Emonuele, Santi Agatino Rizzo, and Nunzio Salerno. "SALHE-EA: A New Evolutionary Algorithm for Multi-Objective Optimization of Electromagnetic Devices." In Studies in Computational Intelligence, 37–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78490-6_5.

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Wahib, Mohamed, Asim Munawar, Masaharu Munetomo, and Kiyoshi Akam. "EA-based Problem Solving Environment over the GRID." In Advances in Evolutionary Algorithms. InTech, 2008. http://dx.doi.org/10.5772/6126.

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Singh, Omveer. "Automatic Generation Control of Multi-Area Interconnected Power Systems Using Hybrid Evolutionary Algorithm." In Handbook of Research on Soft Computing and Nature-Inspired Algorithms, 292–324. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2128-0.ch010.

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A new technique of evaluating optimal gain settings for full state feedback controllers for automatic generation control (AGC) problem based on a hybrid evolutionary algorithms (EA) i.e. genetic algorithm (GA)-simulated annealing (SA) is proposed in this chapter. The hybrid EA algorithm can take dynamic curve performance as hard constraints which are precisely followed in the solutions. This is in contrast to the modern and single hybrid evolutionary technique where these constraints are treated as soft/hard constraints. This technique has been investigated on a number of case studies and gives satisfactory solutions. This technique is also compared with linear quadratic regulator (LQR) and GA based proportional integral (PI) controllers. This proves to be a good alternative for optimal controller's design. This technique can be easily enhanced to include more specifications viz. settling time, rise time, stability constraints, etc.
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Youcef, Bouras. "Research Information." In Advanced Deep Learning Applications in Big Data Analytics, 218–72. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2791-7.ch011.

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This chapter describes the framework of an analytical study around the computational intelligence algorithms, which are prompted by natural mechanisms and complex biological phenomena. These algorithms are numerous and can be classified in two great families: firstly the family of evolutionary algorithms (EA) such as genetic algorithms (GAs), genetic programming (GP), evolutionary strategy (ES), differential evolutionary (DE), paddy field algorithm (PFA); secondly, the swarm intelligence algorithms (SIA) such as particle swarm optimisation (PSO), ant colony optimization (ACO), bacteria foraging optimisation (BFO), wolf colony algorithm (WCA), fireworks algorithm (FA), bat algorithm (BA), cockroaches algorithm (CA), social spiders algorithm (SSA), cuckoo search algorithm (CSA), wasp swarm optimisation (WSO), mosquito optimisation algorithm (MOA). The authors have detailed the functioning of each algorithm following a structured organization (the descent of the algorithm, the inspiration source, the summary, and the general process) that offers for readers a thorough understanding. This study is the fruit of many years of research in the form of synthesis, which groups the contributions offered by several researchers in the meta-heuristic field. It can be the beginning point for planning and modelling new algorithms or improving existing algorithms.
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Conference papers on the topic "Evolutionary algorithms (EA)"

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Lauri, Fabrice, and Abderrafiaa Koukam. "Hybrid ACO/EA algorithms applied to the multi-agent patrolling problem." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900280.

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Muth, David J., Douglas S. McCorkle, Daniel A. Ashlock, and Kenneth M. Bryden. "Developing Multiple Diverse Potential Designs for Heat Transfer Utilizing Graph Based Evolutionary Algorithms." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99560.

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This paper examines the use of graph based evolutionary algorithms (GBEAs) to find multiple acceptable solutions for heat transfer in engineering systems during the optimization process. GBEAs are a type of evolutionary algorithm (EA) in which a topology, or geography, is imposed on an evolving population of solutions. The rates at which solutions can spread within the population are controlled by the choice of topology. As in nature geography can be used to develop and sustain diversity within the solution population. Altering the choice of graph can create a more or less diverse population of potential solutions. The choice of graph can also affect the convergence rate for the EA and the number of mating events required for convergence. The engineering system examined in this paper is a biomass fueled cookstove used in developing nations for household cooking. In this cookstove wood is combusted in a small combustion chamber and the resulting hot gases are utilized to heat the stove’s cooking surface. The spatial temperature profile of the cooking surface is determined by a series of baffles that direct the flow of hot gases. The optimization goal is to find baffle configurations that provide an even temperature distribution on the cooking surface. Often in engineering, the goal of optimization is not to find the single optimum solution but rather to identify a number of good solutions that can be used as a starting point for detailed engineering design. Because of this a key aspect of evolutionary optimization is the diversity of the solutions found. The key conclusion in this paper is that GBEA’s can be used to create multiple good solutions needed to support engineering design.
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Suram, Sunil, and Kenneth M. Bryden. "Solution of a Two-Dimensional Inverse Radiation Problem Using Evolutionary Algorithms." In ASME 2004 Heat Transfer/Fluids Engineering Summer Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ht-fed2004-56339.

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While solving inverse problems stability of the solutions is an issue. Stability ensures that solutions obtained are physically possible and not just mathematically feasible. Techniques like Truncated Singular Value Decomposition (TSVD), Tikhonov’s regularization have been used to stabilize solutions. These are called regularization techniques, which involve selection of regularizing parameters. The choice of these regularizing parameters dictates the accuracy of solutions obtained. In this paper an Evolutionary Algorithm (EA) based optimization procedure is used to solve an inverse radiation problem in a rectangular enclosure. The fitness function and the mutation operator have been designed to eliminate the need for regularization. The evolutionary technique developed in this paper ensures a better search on the fitness landscape, without the additional effort of choosing regularization parameters.
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Neumann, Frank, and Carsten Witt. "Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/665.

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Chance constrained optimization problems allow to model problems where constraints involving stochastic components should only be violated with a small probability. Evolutionary algorithms have been applied to this scenario and shown to achieve high quality results. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for chance constrained optimization. We study the scenario of stochastic components that are independent and Normally distributed. Considering the simple single-objective (1+1)~EA, we show that imposing an additional uniform constraint already leads to local optima for very restricted scenarios and an exponential optimization time. We therefore introduce a multi-objective formulation of the problem which trades off the expected cost and its variance. We show that multi-objective evolutionary algorithms are highly effective when using this formulation and obtain a set of solutions that contains an optimal solution for any possible confidence level imposed on the constraint. Furthermore, we prove that this approach can also be used to compute a set of optimal solutions for the chance constrained minimum spanning tree problem. Experimental investigations on instances of the NP-hard stochastic minimum weight dominating set problem confirm the benefit of the multi-objective approach in practice.
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Ramaswami, Hemant, Yashpal Kovvur, and Sam Anand. "Minimum-Zone Circularity Evaluation Using Particle Swarm Optimization." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81601.

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Robust and accurate evaluation of form tolerances is of paramount importance in today’s world of precision engineering. Present-day Coordinate Measuring Machines (CMMs) operate at high speed and have a high degree of accuracy and repeatability which are capable of meeting the stringent measurement requirements. However, the evaluation algorithms used in conjunction with them are not robust and accurate enough, because of the highly non-linear nature of the minimum-zone circularity formulation. Evolutionary algorithms have proved effective in solving constrained non-linear optimization problems. In this paper, Particle Swarm Optimization (PSO), which is one of the most recent and popular evolutionary algorithms, is employed to evaluate the minimum-zone circularity. The PSO approach imitates the social behavior of organisms such as bird flocking and fish schooling. It differs from other well-known Evolutionary Algorithms (EA) in that each particle of the population, called the swarm, adjusts its trajectory toward its own previous best position, and toward the previous best position attained by any member of its topological neighborhood. The constrained nonlinear model is rewritten as an unconstrained non-linear model using the penalty-function approach. The methodology is validated by testing on several simulated and experimental datasets and yields better results than other existing minimum-zone algorithms.
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Abreu, Bruno T. de, Eliane Martins, and Fabiano L. de Sousa. "Generalized Extremal Optimization: a competitive algorithm for test data generation." In Simpósio Brasileiro de Engenharia de Software. Sociedade Brasileira de Computação, 2007. http://dx.doi.org/10.5753/sbes.2007.21315.

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Software testing is an important part of the software development process, and automating test data generation contributes to reducing cost and time efforts. It has recently been shown that evolutionary algorithms (EAs), such as the Genetic Algorithms (GAs), are valuable tools for test data generation. This work assesses the performance of a recently proposed EA, the Generalized Extremal Optimization (GEO), on test data generation for programs that have paths with loops. Benchmark programs were used as study cases and GEO’s performance was compared to the one of a GA. Results showed that using GEO required much less computational effort than GA on test data generation and also on internal parameter setting. These results indicate that GEO is an attractive option to be used for test data generation.
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Ramirez-Atencia, Cristian, Tobias Benecke, and Sanaz Mostaghim. "T-EA: A Traceable Evolutionary Algorithm." In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185615.

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Akhtar, Shamim, Kang Tai, and Jitendra Prasad. "Topology Optimization of Compliant Mechanisms Using Evolutionary Algorithm With Design Geometry Encoded as a Graph." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34147.

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This paper describes an intuitive way of defining geometry design variables for solving structural topology optimization problems using an evolutionary algorithm (EA). The geometry representation scheme works by defining a skeleton which represents the underlying topology/connectivity of the continuum structure. As the effectiveness of any EA is highly dependent on the chromosome encoding of the design variables, the encoding used here is a graph which reflects this underlying topology so that the genetic crossover and mutation operators of the EA can recombine and preserve any desirable geometric characteristics through succeeding generations of the evolutionary process. The overall optimization procedure is applied to design a straight-line compliant mechanism : a large displacement flexural structure that generates a vertical straight line path at some point when given a horizontal straight line input displacement at another point.
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Huang, Yun, Chaobo Zhang, and Junli Wang. "GNN-EA: Graph Neural Network with Evolutionary Algorithm." In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022. http://dx.doi.org/10.1109/smc53654.2022.9945338.

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Kiyici, Firat, Sefa Yilmazturk, Kahraman Coban, Ercan Arican, Emiliano Costa, and Stefano Porziani. "Rib Cross Section Optimization of a Ribbed Turbine Internal Cooling Channel With Experimental Validation." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64018.

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Due to the recent developments of the engine industry, turbine internal channel cooling is a need. In fact, in order to supply more power efficiently, the fluid temperature at turbine inlet approaches to 2000 K when common turbine materials cannot resist temperature values higher than 1500 K. The crucial point is that the engine cycle efficiency and thrust highly depend on the turbine inlet temperature and, so, such a thermal problem needs to be overcome by cooling. Coolant air of the internal channel cooling systems is mostly taken from valuable compressor bleed that makes it to circulate through the serpentine internal passages. The coolant air flow is commonly fully turbulent, incompressible and with 3D characteristics because of the complex shape of the cooling passage. Considering this latter aspect, the pressure drop also plays a relevant role because, in order to minimize the cooling mass flow, it needs to be reduced. In this study, rib cross-section shape optimization of a ribbed internal cooling channel is conducted to assess the trade-off between two conflicting objectives: heat transfer performance and pressure drop. For this purpose, a novel mesh morphing based optimization tool is developed which uses radial basis functions (RBF) for morphing and meta-model assisted evolutionary algorithms (EA) for optimization. Experimental tests characterized by Reynolds number of 20000 are performed to validate such an optimization tool. The local Nusselt number is calculated using hydraulic diameter of channel and air thermal conductivity corresponding to bulk temperature. The cooling effectiveness of the channel is quantified using the ratio of the Nusselt number of the ribbed case to the Nusselt number of the smooth case. With the gained optimized geometry, the heat transfer shows better results than initial case with a pressure loss improvement of 8%.
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