Evolving graphs with semantic neutral drift

Author:

Atkinson TimothyORCID,Plump DetlefORCID,Stepney SusanORCID

Abstract

AbstractWe introduce the concept of Semantic Neutral Drift (SND) for genetic programming (GP), where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals’ fitness scores. A number of digital circuit benchmark problems have been implemented with rule-based graph programs and empirically evaluated, demonstrating quantitative improvements in evolutionary performance. Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for a GP system if that facilitates the inclusion of SND.

Funder

Engineering and Physical Sciences Research Council

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications

Cited by 5 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. Low-cost and efficient prediction hardware for tabular data using tiny classifier circuits;Nature Electronics;2024-04-25

2. A graph-based framework for model-driven optimization facilitating impact analysis of mutation operator properties;Software and Systems Modeling;2023-01-17

3. Graph representations in genetic programming;Genetic Programming and Evolvable Machines;2021-09-30

4. A study on graph representations for genetic programming;Proceedings of the 2020 Genetic and Evolutionary Computation Conference;2020-06-25

5. Horizontal gene transfer for recombining graphs;Genetic Programming and Evolvable Machines;2020-02-03

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