Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithm with Competitive Image Labeling and Least Square
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Computational complexity and the genetic algorithm
Computational complexity and the genetic algorithm
Dynamic population size in multiobjective evolutionary algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Dynamic populations in genetic algorithms
Proceedings of the 2008 ACM symposium on Applied computing
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
International Journal of Bio-Inspired Computation
IEEE Transactions on Evolutionary Computation
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
Rank-density-based multiobjective genetic algorithm and benchmark test function study
IEEE Transactions on Evolutionary Computation
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid parallel chaos optimization algorithm with harmony search algorithm
Applied Soft Computing
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We set two objectives for this study: one is to emulate chaotic natural populations in GA (Genetic Algorithms) populations by utilizing the Logistic Chaos map model, and the other is to analyze the population fitness distribution by utilizing insect spatial distribution theory. Natural populations are so dynamic that one of the first experimental evidences of Chaos in nature was discovered by a theoretical ecologist, May (1976, Nature, 261,459-467)[30], in his analysis of insect population dynamics. In evolutionary computing, perhaps influenced by the stable or infinite population concepts in population genetics, the status quo of population settings has dominantly been the fixed-size populations. In this paper, we propose to introduce dynamic populations controlled by the Logistic Chaos map model to Genetic Algorithms (GA), and test the hypothesis - whether or not the dynamic populations that emulate chaotic populations in nature will have an advantage over traditional fixed-size populations. The Logistic Chaos map model, arguably the simplest nonlinear dynamics model, has surprisingly rich dynamic behaviors, ranging from exponential, sigmoid growth, periodic oscillations, and aperiodic oscillations, to complete Chaos. What is even more favorable is that, unlike many other population dynamics models, this model can be expressed as a single parameter recursion equation, which makes it very convenient to control the dynamic behaviors and therefore easy to apply to evolutionary computing. The experiments show result values in terms of the fitness evaluations and memory storage requirements. We further conjecture that Chaos may be helpful in breaking neutral space in the fitness landscape, similar to the argument in ecology that Chaos may help the exploration and/or exploitation of environment heterogeneity and therefore enhance a species' survival or fitness.