Geometric semantic genetic programming with normalized and standardized random programs

Geometric semantic genetic programming (GSGP) represents one of the most promising developments in the area of evolutionary computation (EC) in the last decade. The results achieved by incorporating semantic awareness in the evolutionary process demonstrate the impact that geometric semantic operato...

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Veröffentlicht in:Genetic programming and evolvable machines Jg. 25; H. 1
Hauptverfasser: Bakurov, Illya, Muñoz Contreras, José Manuel, Castelli, Mauro, Rodrigues, Nuno, Silva, Sara, Trujillo, Leonardo, Vanneschi, Leonardo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2024
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Biomedical Engineering and Bioengineering
Compilers
Computer Science
Deep learning
Electrical Engineering
Evolutionary computation
Genetic algorithms
Interpreters
Operators (mathematics)
Programming Languages
Programming Techniques
Semantics
Software Engineering/Programming and Operating Systems
Standardization
Geometric semantic mutation
Internal covariate shift
Sigmoid distribution bias
Model simplification
ISSN:1389-2576, 1573-7632
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Zusammenfassung:Geometric semantic genetic programming (GSGP) represents one of the most promising developments in the area of evolutionary computation (EC) in the last decade. The results achieved by incorporating semantic awareness in the evolutionary process demonstrate the impact that geometric semantic operators have brought to the field of EC. An improvement to the geometric semantic mutation (GSM) operator is proposed, inspired by the results achieved by batch normalization in deep learning. While, in one of its most used versions, GSM relies on the use of the sigmoid function to constrain the semantics of two random programs responsible for perturbing the parent’s semantics, here a different approach is followed, which allows reducing the size of the resulting programs and overcoming the issues associated with the use of the sigmoid function, as commonly done in deep learning. The idea is to consider a single random program and use it to perturb the parent’s semantics only after standardization or normalization. The experimental results demonstrate the suitability of the proposed approach: despite its simplicity, the presented GSM variants outperform standard GSGP on the studied benchmarks, with a difference in terms of performance that is statistically significant. Furthermore, the individuals generated by the new GSM variants are easier to simplify, allowing us to create accurate but significantly smaller solutions.
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ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-024-09479-1