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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 |
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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 |
Downloads: | 0 |
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