Result Details
Towards Efficient Semantic Mutation in CGP: Enhancing SOMOk
Genetic Programming (GP) and its variants have proven to be promising techniques
for solving problems across various domains. However, GP does not scale well,
particularly when applied to symbolic regression in the Boolean domain. To
address this limitation, a semantically oriented mutation operator (SOMO) has
been proposed and integrated with Cartesian Genetic Programming (CGP).
Nevertheless, like standard GP, even SOMO suffers in some cases from bloat - an
excessive growth in solution size without a corresponding performance gain. This
work introduces SOMOk-TS, an extension of SOMO that incorporates the so-called
Tumor Search strategy to identify and preserve reusable substructures. By
managing diversity through an immune-inspired mechanism, SOMOk-TS promotes the
reuse of substructures, thereby reducing computational overhead. It achieves
significantly lower execution times while maintaining or improving solution
compactness, highlighting its potential for scalable and efficient evolutionary
design.
Genetic Programming, Boolean function learning
@inproceedings{BUT197538,
author="Lukáš {Plevač} and Zdeněk {Vašíček}",
title="Towards Efficient Semantic Mutation in CGP: Enhancing SOMOk",
booktitle="Proceedings of the Genetic and Evolutionary Computation Conference Companion",
year="2025",
pages="2172--2176",
publisher="Association for Computing Machinery",
address="Malaga",
doi="10.1145/3712255.3734289",
isbn="979-8-4007-1464-1"
}