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Measuring the effects of increasing dimensionality on fitness-based selection and failed exploration

Citation

Chen, S and Bolufe-Rohler, A and Montgomery, J and Tamayo-Vera, D and Hendtlass, T, Measuring the effects of increasing dimensionality on fitness-based selection and failed exploration, Proceedings of 2022 IEEE Congress on Evolutionary Computation (CEC), 18-23 July 2022, Padua, Italy, pp. 1-8. ISBN 9781665467087 (2022) [Refereed Conference Paper]

Copyright Statement

Copyright 2022 IEEE

DOI: doi:10.1109/CEC55065.2022.9870409

Abstract

The rate of Successful Exploration is related to the proportion of search solutions from fitter attraction basins that are fitter than the current reference solution. A reference solution that moves closer to its local optimum (i.e. experiences exploitation) will reduce the proportion of these fitter solutions, and this can lead to decreased rates of Successful Exploration/increased rates of Failed Exploration. This effect of Fitness-Based Selection is studied in Particle Swarm Optimization and Differential Evolution with increasing dimensionality of the search space. It is shown that increasing rates of Failed Exploration represent another aspect of the Curse of Dimensionality that needs to be addressed by metaheuristic design.

Item Details

Item Type:Refereed Conference Paper
Keywords:exploration, exploitation, fitness-based selection, curse of dimensionality, particle swarm optimization, differential evolution
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Evolutionary computation
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:154070
Year Published:2022
Deposited By:Information and Communication Technology
Deposited On:2022-10-26
Last Modified:2023-01-11
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