Results 11 to 20 of about 3,829,483 (294)
Extending Genetic Algorithms with Biological Life-Cycle Dynamics [PDF]
BiomimeticsIn this paper, we aim to enhance genetic algorithms (GAs) by integrating a dynamic model based on biological life cycles. This study addresses the challenge of maintaining diversity and adaptability in GAs by incorporating stages of birth, growth ...
J. C. Felix-Saul +3 more
doaj +2 more sources
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
core +5 more sources
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
core +1 more source
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
core +1 more source
Recombination and Self-Adaptation in Multi-objective Genetic Algorithms [PDF]
, 2004 This paper investigates the influence of recombination and self-adaptation in real-encoded Multi-Objective Genetic Algorithms (MOGAs). NSGA-II and SPEA2 are used as example to characterize the efficiency of MOGAs in relation to various recombination ...
Régnier, Jérémi +5 more
core +1 more source
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
core +1 more source
Proceedings of the Genetic and Evolutionary Computation Conference, 2017 For genetic algorithms using a bit-string representation of length~$n$, the general recommendation is to take $1/n$ as mutation rate. In this work, we discuss whether this is really justified for multimodal functions. Taking jump functions and the $(1+1)$ evolutionary algorithm as the simplest example, we observe that larger mutation rates give ...
Benjamin Doerr +3 more
openaire +2 more sources
CoRR, 2021 Death has long been overlooked in evolutionary algorithms. Recent research has shown that death (when applied properly) can benefit the overall fitness of a population and can outperform sub-sections of a population that are "immortal" when allowed to evolve together in an environment [1].
Micah Burkhardt, Roman V. Yampolskiy
openaire +3 more sources
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
core +4 more sources
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.
P. J. Bentley +3 more
core +3 more sources