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Using average-fitness based selection to combat the curse of dimensionality

Citation

Chen, S and Bolufe-Rohler, A and Montgomery, J and Zhang, W and Hendtlass, T, Using average-fitness based selection to combat the curse of dimensionality, 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/CEC55065.2022.9870232

Abstract

It is well known that metaheuristics for numerical optimization tend to decrease in performance as dimensionality increases. These effects are commonly referred to as "The Curse of Dimensionality". An obvious change to search spaces with increasing dimensionality is that their volume grows exponentially, and this has led to large amounts of research on improved exploration. A recent insight is that the shape of attraction basins can also change drastically with increasing dimensionality, and this has led to selection-based approaches to combat the Curse of Dimensionality. Average-Fitness Based Selection is introduced as a means to reduce the selection errors caused by Fitness-Based Selection. Experimental results show that the rate of selection errors grows much more slowly for Average-Fitness Based Selection with Increasing dimensionality.

Item Details

Item Type:Refereed Conference Paper
Keywords:selection, exploration, metaheuristic, curse of dimensionality
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:154069
Year Published:2022
Deposited By:Information and Communication Technology
Deposited On:2022-10-26
Last Modified:2023-01-11
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