Productive fitness in diversity-aware evolutionary algorithms

Author:

Gabor Thomas,Phan ThomyORCID,Linnhoff-Popien Claudia

Abstract

AbstractIn evolutionary algorithms, the notion of diversity has been adopted from biology and is used to describe the distribution of a population of solution candidates. While it has been known that maintaining a reasonable amount of diversity often benefits the overall result of the evolutionary optimization process by adjusting the exploration/exploitation trade-off, little has been known about what diversity is optimal. We introduce the notion of productive fitness based on the effect that a specific solution candidate has some generations down the evolutionary path. We derive the notion of final productive fitness, which is the ideal target fitness for any evolutionary process. Although it is inefficient to compute, we show empirically that it allows for an a posteriori analysis of how well a given evolutionary optimization process hit the ideal exploration/exploitation trade-off, providing insight into why diversity-aware evolutionary optimization often performs better.

Funder

Ludwig-Maximilians-Universität München

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Study of Fitness Gains in Evolving Finite State Machines;Lecture Notes in Computer Science;2023-11-27

2. Self-Replication in Neural Networks;Artificial Life;2022

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