RGFGA: An efficient representation and crossover for grouping genetic algorithms [PDF]
, 2005 There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree.
Crampton, J, Tucker, A, Swift, S
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Hierarchical Crossover in Genetic Algorithms [PDF]
, 1996 This paper identifies the limitations of conventional crossover in genetic algorithms when operating on two chromosomes of differing lengths. To address these problems, the concept of a Semantic Hierarchy (i.e.
Wakefield, Jonathan P. +1 more
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Learning the dominance in diploid genetic algorithms for changing optimization problems [PDF]
, 2007 Using diploid representation with dominance scheme is one of the approaches developed for genetic algorithms to address dynamic optimization problems.
Yang, S
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Memory-based immigrants for genetic algorithms in dynamic environments [PDF]
, 2005 Copyright @ 2005 ACMInvestigating and enhancing the performance of genetic algorithms in dynamic environments have attracted a growing interest from the community of genetic algorithms in recent years.
Yang, S +2 more
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Optimisation of the weighting functions of an H∞ controller using genetic algorithms and structured genetic algorithms [PDF]
, 2008 In this paper the optimisation of the weighting functions for an H<sub>∞</sub> controller using genetic algorithms and structured genetic algorithms is considered. The choice of the weighting functions is one of the key steps in the design of
McGookin, E.W. +2 more
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Niching genetic algorithms for optimization in electromagnetics. I. Fundamentals [PDF]
, 1998 Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions in the search space. In this paper, we review and discuss various strategies of niching for optimization in electromagnetics.
Sareni, Bruno +2 more
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Genetic algorithms with memory- and elitism-based immigrants in dynamic environments [PDF]
, 2008 Copyright @ 2008 by the Massachusetts Institute of TechnologyIn recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised.
Yang, S, Yang, Shengxiang
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A comparative study of immune system based genetic algorithms in dynamic environments [PDF]
, 2006 Copyright @ 2006 ACMDiversity and memory are two major mechanisms used in biology to keep the adaptability of organisms in the ever-changing environment in nature.
Yang, S +2 more
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Multiobjective genetic algorithms with application to control engineering problems. [PDF]
, 1995 Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number of characteristics particularly useful for applications in optimization, engineering, and ...
Fonseca, Carlos Manuel Mira da
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Cosmological Parameter Estimation with Genetic Algorithms
Universe, 2023 Genetic algorithms are a powerful tool in optimization for single and multimodal functions. This paper provides an overview of their fundamentals with some analytical examples.
Ricardo Medel-Esquivel +4 more
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