Title
Evolving Graphs With Semantic Neutral Drift
Abstract
We 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.
Year
DOI
Venue
2021
10.1007/s11047-019-09772-4
NATURAL COMPUTING
Keywords
DocType
Volume
Genetic programming, Evolutionary algorithms, Neutral drift, Semantic equivalence, Mutation operators, Graph programming
Journal
20
Issue
ISSN
Citations 
1
1567-7818
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Timothy Atkinson151.80
Detlef Plump260462.14
Susan Stepney3813113.21