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Relevant bibliographies by topics / Evolutionary Algorithms

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Academic literature on the topic 'Evolutionary Algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 July 2024

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Journal articles on the topic "Evolutionary Algorithms"

1

Khera, Vansh. "Comparative Study of Evolutionary Algorithms." International Journal of Science and Research (IJSR) 12, no. 6 (June 5, 2023): 836–40. http://dx.doi.org/10.21275/sr23610122607.

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Agapie, Alexandru. "Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms." Evolutionary Computation 9, no. 2 (June 2001): 127–46. http://dx.doi.org/10.1162/106365601750190370.

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Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.

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Bäck, Thomas. "Evolutionary algorithms." ACM SIGBIO Newsletter 12, no. 2 (June 1992): 26–31. http://dx.doi.org/10.1145/130686.130691.

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Graña, Manuel. "Evolutionary algorithms." Information Sciences 133, no. 3-4 (April 2001): 101–2. http://dx.doi.org/10.1016/s0020-0255(01)00079-2.

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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 (April 24, 2014): 178–95. http://dx.doi.org/10.1002/widm.1124.

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Dioşan, Laura, and Mihai Oltean. "Evolutionary design of Evolutionary Algorithms." Genetic Programming and Evolvable Machines 10, no. 3 (March 20, 2009): 263–306. http://dx.doi.org/10.1007/s10710-009-9081-6.

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Nico, Nico, Novrido Charibaldi, and Yuli Fauziah. "Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 1 (May 30, 2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.

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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 (October 11, 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-population evolutionary algorithm adapting to complementary evolutionary strategy (DPCEDT) based on the study of differential evolution algorithm, teaching and learning-based optimization algorithm. The simulation results show that the algorithm performs better than the TLBO-DE, HDT and DPDT and some other algorithms do in most test functions. It suggests that the complementary evolutionary strategies are more advantageous than other evolutionary strategies in dual-population evolutionary algorithms.

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Leciejewski, Sławomir, and Mariusz Szynkiewicz. "Algorithmicity of Evolutionary Algorithms." Studies in Logic, Grammar and Rhetoric 63, no. 1 (September 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 will try to answer the question whether – and to what extent – they are still algorithms in the classical sense.

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Afathi, Maan. "Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems." Eastern-European Journal of Enterprise Technologies 6, no. 4 (114) (December 16, 2021): 6–14. http://dx.doi.org/10.15587/1729-4061.2021.245222.

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The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

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Dissertations / Theses on the topic "Evolutionary Algorithms"

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Reimann, Axel. "Evolutionary algorithms and optimization." Doctoral thesis, [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=969093497.

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Ciftci, Erhan. "Evolutionary Algorithms In Design." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12607983/index.pdf.

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Evolutionary Structural Optimization (ESO) is a relatively new design tool used to improve and optimise the design of structures. In this method, a few elements of an initial design domain of finite elements are iteratively removed. Such a process is carried out repeatedly until an optimum design is achieved, or until a desired given area or volume is reached. In structural design, there is the demand for the development of design tools and methods that includes optimization. This need is the reason behind the development of methods like Evolutionary Structural Optimization (ESO). It is also this demand that this thesis seeks to satisfy. This thesis develops and examines the program named EVO, with the concept of structural optimization in the ESO process. Taking into account the stiffness and stress constraints, EVO allows a realistic and accurate approach to optimising a model in any given environment. Finally, in verifying the ESO algorithm&rsquo
s and EVO program&rsquo
s usefulness to the practical aspect of design, the work presented herein applies the ESO method to case studies. They concern the optimization of 2-D frames, and the optimization of 3-D spatial frames and beams with the prepared program EVO. Comparisons of these optimised models are then made to those that exist in literature.

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Loshchilov, Ilya. "Surrogate-Assisted Evolutionary Algorithms." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00823882.

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Les Algorithmes Évolutionnaires (AEs) ont été très étudiés en raison de leur capacité à résoudre des problèmes d'optimisation complexes en utilisant des opérateurs de variation adaptés à des problèmes spécifiques. Une recherche dirigée par une population de solutions offre une bonne robustesse par rapport à un bruit modéré et la multi-modalité de la fonction optimisée, contrairement à d'autres méthodes d'optimisation classiques telles que les méthodes de quasi-Newton. La principale limitation de AEs, le grand nombre d'évaluations de la fonction objectif, pénalise toutefois l'usage des AEs pour l'optimisation de fonctions chères en temps calcul. La présente thèse se concentre sur un algorithme évolutionnaire, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), connu comme un algorithme puissant pour l'optimisation continue boîte noire. Nous présentons l'état de l'art des algorithmes, dérivés de CMA-ES, pour résoudre les problèmes d'optimisation mono- et multi-objectifs dans le scénario boîte noire. Une première contribution, visant l'optimisation de fonctions coûteuses, concerne l'approximation scalaire de la fonction objectif. Le meta-modèle appris respecte l'ordre des solutions (induit par la valeur de la fonction objectif pour ces solutions) ; il est ainsi invariant par transformation monotone de la fonction objectif. L'algorithme ainsi défini, saACM-ES, intègre étroitement l'optimisation réalisée par CMA-ES et l'apprentissage statistique de meta-modèles adaptatifs ; en particulier les meta-modèles reposent sur la matrice de covariance adaptée par CMA-ES. saACM-ES préserve ainsi les deux propriété clé d'invariance de CMA-ES~: invariance i) par rapport aux transformations monotones de la fonction objectif; et ii) par rapport aux transformations orthogonales de l'espace de recherche. L'approche est étendue au cadre de l'optimisation multi-objectifs, en proposant deux types de meta-modèles (scalaires). La première repose sur la caractérisation du front de Pareto courant (utilisant une variante mixte de One Class Support Vector Machone (SVM) pour les points dominés et de Regression SVM pour les points non-dominés). La seconde repose sur l'apprentissage d'ordre des solutions (rang de Pareto) des solutions. Ces deux approches sont intégrées à CMA-ES pour l'optimisation multi-objectif (MO-CMA-ES) et nous discutons quelques aspects de l'exploitation de meta-modèles dans le contexte de l'optimisation multi-objectif. Une seconde contribution concerne la conception d'algorithmes nouveaux pour l'optimi\-sation mono-objectif, multi-objectifs et multi-modale, développés pour comprendre, explorer et élargir les frontières du domaine des algorithmes évolutionnaires et CMA-ES en particulier. Spécifiquement, l'adaptation du système de coordonnées proposée par CMA-ES est couplée à une méthode adaptative de descente coordonnée par coordonnée. Une stratégie adaptative de redémarrage de CMA-ES est proposée pour l'optimisation multi-modale. Enfin, des stratégies de sélection adaptées aux cas de l'optimisation multi-objectifs et remédiant aux difficultés rencontrées par MO-CMA-ES sont proposées.

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Maitre, Ogier. "GPGPU for Evolutionary Algorithms." Strasbourg, 2011. http://www.theses.fr/2011STRA6240.

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Les algorithmes évolutionnaires permettent de trouver des réponses satisfaisantes, mais non-nécessairement optimales à des problèmes complexes. La puissance de ces algorithmes est directement corrélée à la puissance de calcul disponible pour leur exécution. En effet, ces algorithmes réalisent une exploration en parallèle de l’espace de recherche, par le biais de l’évolution d’une population d’individus plus ou moins adaptés à la résolution du problème. La puissance de calcul disponible contraint la taille de la population et donc la capacité d’exploration ou d’exploitation qu’offre un algorithme. Parallèlement, nous assistons au développement des architectures de processeurs multi-cœurs. Ces processeurs peuvent contenir jusqu’à plusieurs centaines de cœurs dans une puce, mais possèdent des contraintes structurelles qui imposent une adaptation des algorithmes utilisés. Parmi ces processeurs se développent depuis 2007 les processeurs de type GPGPU (pour General Purpose Graphical Processing Unit), qui sont des versions généralisées de puces de rendu 3D. Ces processeurs possèdent jusqu’à plusieurs centaines de cœurs par puce et permettent d’obtenir des accélérations de plusieurs centaines de fois, sur certaines applications. Cette thèse détaille l’utilisation de telles architectures dans le cadre des algorithmes évolutionnaires. Plusieurs variantes de ces algorithmes sont étudiées, comme la AG / SE et la GP. Des implantations sur architectures mixtes et GPGPUs seules sont étudiées, par l’application à des problèmes jouets et réels
Evolutionary algorithms can find satisfactory, but not necessarily optimal solutions to complex problems. The power of these algorithms is directly related to the available computing power. Indeed, these algorithms perform a parallel exploration of the search space, through the evolution of a population of individuals more or less suited to the problem being solved. The available computing power has a direct impact on the population size and therefore the exploration/exploitation ability of a given algorithm. On the other hand, multicore processor architectures are being developed largely nowadays. These processors can contain up to hundreds of cores in a chip, but have structural constraints that impose an adaptation of the algorithms to be ported on. Among multicore processors, GPGPU-type (for General Purpose Graphical Processing Unit) processors, which are generalized versions of 3D rendering chips, have been industrially developed since 2007. These processors have up to hundreds of cores per chip and allow to obtain speedups of several hundred times, on some applications. This thesis details the use of such architectures in the context of evolutionary algorithms. Several variants of these algorithms are studied, such as GA / ES and GP. Implementation on mixed architectures and GPGPUs only were considered, using artificial and real-world application

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Rohlfshagen, Philipp. "Molecular Algorithms for Evolutionary Computation." Thesis, University of Birmingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522032.

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Smith, James Edward. "Self adaptation in evolutionary algorithms." Thesis, University of the West of England, Bristol, 1998. http://eprints.uwe.ac.uk/11046/.

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Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population, using a number of parameterised genetic operators. In this thesis the phenomenon of Self Adaptation of the genetic operators is investigated. A new framework for classifying adaptive algorithms is proposed, based on the scope of the adaptation, and on the nature of the transition function guiding the search through the space of possible configurations of the algorithm. Mechanisms are investigated for achieving the self adaptation of recombination and mutation operators within a genetic algorithm, and means of combining them are investigated. These are shown to produce significantly better results than any of the combinations of fixed operators tested, across a range of problem types. These new operators reduce the need for the designer of an algorithm to select appropriate choices of operators and parameters, thus aiding the implementation of genetic algorithms. The nature of the evolving search strategies are investigated and explained in terms of the known properties of the landscapes used, and it is suggested how observations of evolving strategies on unknown landscapes may be used to categorise them, and guide further changes in other facets of the genetic algorithm. This work provides a contribution towards the study of adaptation in Evolutionary Algorithms, and towards the design of robust search algorithms for “real world” problems.

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Williams, Kenneth Peter. "Evolutionary algorithms for automatic parallelization." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265665.

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Shen, Liang. "Evolutionary algorithms with mixed strategy." Thesis, Aberystwyth University, 2016. http://hdl.handle.net/2160/f08f9fe9-f4d1-48cd-aa17-3218eb2f4f35.

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During the last several decades, many kinds of population based Evolutionary Algorithms have been developed and considerable work has been devoted to computational methods which are inspired by biological evolution and natural selection, such as Evolutionary Programming and Clonal Selection Algorithm. The objective of these algorithms is not only to find suitable adjustments to the current population and hence the solution, but also to perform the process efficiently. However, a parameter setting that was optimal at the beginning of the algorithm may become unsuitable during the evolutionary process. Thus, it is preferable to automatically modify the control parameters during the runtime process. The approach required could have a bias on the distribution towards appropriate directions of the search space, thereby maintaining sufficient diversity among individuals in order to enable further ability of evolution. This thesis has offered an initial approach to developing this idea. The work starts from a clear understanding of the literature that is of direct relevance to the aforementioned motivations. The development of this approach has been built upon the basis of the fundamental and generic concepts of evolutionary algorithms. The work has exploited and benefited from a range of representative evolutionary computational mechanisms. In particular, essential issues in evolutionary algorithms such as parameter control, including the general aspects of parameter tuning and typical means for implementing parameter control have been investigated. Both the hyperheuristic algorithm and the memetic algorithm have set up a comparative work for the present development. This work has developed several novel techniques that contribute towards the advancement of evolutionary computation and optimization. One such novel approach is to construct a mixed strategy based on the concept of local fitness landscape. It exploits the concepts of fitness landscape and local fitness landscape. Both theoretical description and experimental investigation of this local fitness landscape-based mixed strategy have been provided, and systematic comparisons with alternative approaches carried out. Another contribution of this thesis is the innovative application of mixed strategy. This is facilitated by encompassing two mutation operators into the mixed strategy, which are borrowed from classical differential evolution techniques. Such an improved method has been shown to be simple and easy for implementation. The work has been utilised to deal with the problem of protein folding in bioinformatics. It is demonstrated that the proposed algorithm possesses an appropriate balance between exploration and exploitation. The use of this improved algorithm is less likely to fall into local optimal, entailing a faster and better convergence in resolving challenging realistic application problems.

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Srikanth, Veturi. "Evolutionary algorithms for currency trading." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619749.

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Karunarathne, Lalith. "Network coding via evolutionary algorithms." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/57047/.

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Network coding (NC) is a relatively recent novel technique that generalises network operation beyond traditional store-and-forward routing, allowing intermediate nodes to combine independent data streams linearly. The rapid integration of bandwidth-hungry applications such as video conferencing and HDTV means that NC is a decisive future network technology. NC is gaining popularity since it offers significant benefits, such as throughput gain, robustness, adaptability and resilience. However, it does this at a potential complexity cost in terms of both operational complexity and set-up complexity. This is particularly true of network code construction. Most NC problems related to these complexities are classified as non deterministic polynomial hard (NP-hard) and an evolutionary approach is essential to solve them in polynomial time. This research concentrates on the multicast scenario, particularly: (a) network code construction with optimum network and coding resources; (b) optimising network coding resources; (c) optimising network security with a cost criterion (to combat the unintentionally introduced Byzantine modification security issue). The proposed solution identifies minimal configurations for the source to deliver its multicast traffic whilst allowing intermediate nodes only to perform forwarding and coding. In the method, a preliminary process first provides unevaluated individuals to a search space that it creates using two generic algorithms (augmenting path and linear disjoint path). An initial population is then formed by randomly picking individuals in the search space. Finally, the Multi-objective Genetic algorithm (MOGA) and Vector evaluated Genetic algorithm (VEGA) approaches search the population to identify minimal configurations. Genetic operators (crossover, mutation) contribute to include optimum features (e.g. lower cost, lower coding resources) into feasible minimal configurations. A fitness assignment and individual evaluation process is performed to identify the feasible minimal configurations. Simulations performed on randomly generated acyclic networks are used to quantify the performance of MOGA and VEGA.

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Books on the topic "Evolutionary Algorithms"

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Davis, Lawrence David, Kenneth De Jong, Michael D. Vose, and L. Darrell Whitley, eds. Evolutionary Algorithms. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1542-4.

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Pétrowski, Alain, and Sana Ben-Hamida. Evolutionary Algorithms. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119136378.

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Spears, William M. Evolutionary Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04199-4.

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Kita, Eisuke. Evolutionary algorithms. Rijeka: InTech, 2011.

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Abraham, Ajith, Crina Grosan, and Hisao Ishibuchi, eds. Hybrid Evolutionary Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6.

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Jansen, Thomas. Analyzing Evolutionary Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-17339-4.

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Crina, Grosan, Abraham Ajith 1968-, and Ishibuchi Hisao, eds. Hybrid evolutionary algorithms. Berlin: Springer, 2007.

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Jörg, Biethahn, and Nissen Volker 1965-, eds. Evolutionary algorithms in management applications. Berlin: Springer, 1995.

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Yu, Xinjie, and Mitsuo Gen. Introduction to Evolutionary Algorithms. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-129-5.

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

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Book chapters on the topic "Evolutionary Algorithms"

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Druckmann, Shaul. "Evolutionary Algorithms." In Encyclopedia of Computational Neuroscience, 1152–58. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_159.

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Michalewicz, Zbigniew, Robert Hinterding, and Maciej Michalewicz. "Evolutionary Algorithms." In Fuzzy Evolutionary Computation, 3–31. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_1.

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Pappa, Gisele L., and Alex A. Freitas. "Evolutionary Algorithms." In Natural Computing Series, 47–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02541-9_3.

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Drechsler, Rolf. "Evolutionary Algorithms." In Evolutionary Algorithms for VLSI CAD, 11–17. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2866-8_2.

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Schoenauer, Marc. "Evolutionary Algorithms." In Handbook of Evolutionary Thinking in the Sciences, 621–35. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9014-7_28.

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Mattfeld, Dirk C. "Evolutionary Algorithms." In Evolutionary Search and the Job Shop, 49–64. Heidelberg: Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-662-11712-5_4.

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Grosan, Crina, and Ajith Abraham. "Evolutionary Algorithms." In Intelligent Systems Reference Library, 345–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21004-4_14.

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Langford, John, Xinhua Zhang, Gavin Brown, Indrajit Bhattacharya, Lise Getoor, Thomas Zeugmann, Thomas Zeugmann, et al. "Evolutionary Algorithms." In Encyclopedia of Machine Learning, 332. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_270.

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Eiben, Ágoston E., and James E. Smith. "Evolutionary Algorithms." In Handbook of Memetic Algorithms, 9–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23247-3_2.

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Petrowski, Alain, and Sana Ben Hamida. "Evolutionary Algorithms." In Metaheuristics, 115–78. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45403-0_6.

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Conference papers on the topic "Evolutionary Algorithms"

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Diosan, Laura Silvia, and Mihai Oltean. "Evolving evolutionary algorithms using evolutionary algorithms." In the 2007 GECCO conference companion. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274008.

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Merelo, Juan J. "Fluid evolutionary algorithms." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586476.

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Lourenço, Nuno, Francisco Pereira, and Ernesto Costa. "Evolving evolutionary algorithms." In the fourteenth international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330794.

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Doerr, Benjamin, Mahmoud Fouz, and Carsten Witt. "Quasirandom evolutionary algorithms." In the 12th annual conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830483.1830749.

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Hitomi, Nozomi, and Daniel Selva. "A Hyperheuristic Approach to Leveraging Domain Knowledge in Multi-Objective Evolutionary Algorithms." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59870.

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Evolutionary algorithms have shown much success in solving real-world design problems, but they are considered computationally inefficient because they rely on many objective-function evaluations instead of leveraging domain knowledge to guide the optimization. An evolutionary algorithm’s performance can be improved by utilizing operators called domain-specific heuristics that incorporate domain knowledge, but existing knowledge-intensive algorithms utilize one or two domain-specific heuristics, which limits the amount of incorporated knowledge or treats all knowledge as equally effective. We propose a hyperheuristic approach that efficiently utilizes multiple domain-specific heuristics that incorporate knowledge from different sources by allocating computational resources to the effective ones. Furthermore, a hyperheuristic allows the simultaneous use of conventional evolutionary operators that assist in escaping local optima. This paper empirically demonstrates the efficacy of the proposed hyperheuristic approach on a multi-objective design problem for an Earth observation satellite system. Results show that the hyperheuristic approach significantly improves the search performance compared to an evolutionary algorithm that does not use any domain knowledge.

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Scott, Eric O., and Kenneth A. De Jong. "Understanding Simple Asynchronous Evolutionary Algorithms." In FOGA '15: Foundations of Genetic Algorithms XIII. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2725494.2725509.

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Goldberg, Leslie Ann. "Evolutionary Dynamics on Graphs." In FOGA '15: Foundations of Genetic Algorithms XIII. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2725494.2725495.

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Corns, Steven M., Kenneth M. Bryden, and Daniel A. Ashlock. "Evolutionary Optimization Using Graph Based Evolutionary Algorithms." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-41287.

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Graph based evolutionary algorithms (GBEAs) are a novel evolutionary optimization technique that utilize population graphing to impose a topology or geography on the evolving solution set. In many cases in nature, the ability of a particular member of a population to mate and reproduce is limited. The factors creating these limits vary widely and include geographical distance, mating rituals, and others. The effect of these factors is to limit the mating pool, reducing the rate of spread of genetic characteristics, and increased diversity within the population. GBEAs mimic these factors resulting in increased diversity within the solution population. When properly tuned to the problem and the size of the population set, GBEAs can result in improved convergence times and a more diverse number of viable solutions. This paper examines the impact of the fitness landscape, population size, and choice of graph on the evolutionary process. In general, it was found that there was an optimal population size and graph combination for each problem. This optimal graph/population was problem dependent.

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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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Jesus, Alexandre D., Arnaud Liefooghe, Bilel Derbel, and Luís Paquete. "Algorithm selection of anytime algorithms." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377930.3390185.

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Reports on the topic "Evolutionary Algorithms"

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Bosl, W. Simulation of Biochemical Pathway Adaptability Using Evolutionary Algorithms. Office of Scientific and Technical Information (OSTI), January 2005. http://dx.doi.org/10.2172/917509.

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Olson, Colin C., Jonathan M. Nichols, Carl A. Villarruel, and Frank Bucholtz. On the Potential Use of Evolutionary Algorithms for Electro-Optic System Design. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada544028.

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin, and Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

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Homaifar, Abdollah, Albert Esterline, and Bahram Kimiaghalam. Hybrid Projected Gradient-Evolutionary Search Algorithm for Mixed Integer Nonlinear Optimization Problems. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada455904.

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ZHANG, Dongmei, Hui JIN, and Wei LIU. Spatial Trend Surface Analysis and Geochemical Anomaly Evaluation Based on Two Stage GEP Evolutionary Algorithm. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0116.

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