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Using average-fitness based selection to combat the curse of dimensionality
conference contribution
posted on 2023-05-23, 15:32 authored by Chen, S, Bolufe-Rohler, A, Erin MontgomeryErin Montgomery, Zhang, W, Hendtlass, TIt 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.
History
Publication title
Proceedings of 2022 IEEE Congress on Evolutionary Computation (CEC)Pagination
1-8ISBN
9781665467087Department/School
School of Information and Communication TechnologyPublisher
IEEEEvent title
2022 IEEE Congress on Evolutionary Computation (CEC)Event Venue
Padua, ItalyDate of Event (Start Date)
1996-01-01Date of Event (End Date)
1996-01-01Rights statement
Copyright 2022 IEEERepository Status
- Restricted