Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes
Applied Intelligence
Controlling Crossover through Inductive Learning
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Effects of diversity control in single-objective and multi-objective genetic algorithms
Journal of Heuristics
Genetic algorithms for modelling and optimisation
Journal of Computational and Applied Mathematics - Special issue: Mathematics applied to immunology
Reducing population size while maintaining diversity
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Analysis and modeling of control tasks in dynamic systems
IEEE Transactions on Evolutionary Computation
Application of an Ordinal Optimization Algorithm to the Wafer Testing Process
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Journal on Selected Areas in Communications
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The performance of evolutionary algorithms (EAs) may heavily depend severely on a suitable choice of parameters such as mutation and crossover rates. Several methods to adjust those parameters have been developed in order to enhance EA performance. For this purpose, it is important to understand the EA dynamics, i.e., to appreciate the behavior of the population. Hence, this paper presents a new model of population dynamics to describe and predict the diversity in any particular generation. The formulation is based on selecting the probability density function of each individual. The population dynamics proposed is modeled for a generational population. The model was tested in several case studies of different population sizes. The results suggest that the prediction error decreases as the population size increases.