Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Advances in Engineering Software
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptively Resizing Populations: An Algorithm and Analysis
Proceedings of the 5th International Conference on Genetic Algorithms
Dimensional Analysis of Allele-Wise Mixing Revisited
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A self-adaptive multiagent evolutionary algorithm for electrical machine design
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A fast grasp synthesis method for online manipulation
Robotics and Autonomous Systems
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A Genetic Algorithm (GA) is proposed that uses a variable population size in the form of a saw-tooth function. The aim is to enhance the overall behaviour of the algorithm relying on the dynamics of evolution of the GA in a way that magnifies its efficiency. The proposed scheme is applied into two categories of problems often used as benchmark tests. These correspond to two n-dimensional multimodal peak functions with different features. Numerical results are presented for a wide range of parameters. The main finding is that for large amplitudes and a broad range of values for the period of variation of the population size, the overall performance of the proposed scheme reaches the performance of a Standard GA of substantial bigger population size. This trend is justified also on the basis of schema theorem.