Abstract
Evolutionary Algorithms (EAs) are a useful tool to tackle real‐world optimisation problems. Two important features that make these problems hard are multimodality and high dimensionality of the search landscape.
In this paper, we present a real‐parameter Genetic Algorithm (GA) which is effective in optimising high dimensional, multimodal functions. We compare our algorithm with a previously published GA which the authors claim gives good results for high dimensional, multimodal functions. For problems with only few local optima, our algorithm does not perform as well as the other algorithm. However, for a problem with very many local optima, our algorithm performed significantly better.
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References
Ballester, P. J. and Carter, J. N. (2003) Real-parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization. Proceedings of the Genetic and Evolutionary Computation Conference, Ed. Erick Cantú-Paz et al. (Lecture Notes in Computer Science, Springer), 706–717.
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© 2004 Springer-Verlag London
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Ballester, P.J., Carter, J.N. (2004). An Effective Real-Parameter Genetic Algorithm for Multimodal Optimisation. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture VI. Springer, London. https://doi.org/10.1007/978-0-85729-338-1_30
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DOI: https://doi.org/10.1007/978-0-85729-338-1_30
Publisher Name: Springer, London
Print ISBN: 978-1-85233-829-9
Online ISBN: 978-0-85729-338-1
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