Semantic approximation for reducing code bloat in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{NGUYEN:2020:swarm,

• author = "Quang Uy Nguyen and Thi Huong Chu",
• title = "Semantic approximation for reducing code bloat in Genetic Programming",
• journal = "Swarm and Evolutionary Computation",
• volume = "58",
• pages = "100729",
• year = "2020",
• ISSN = "2210-6502",
• DOI = "doi:10.1016/j.swevo.2020.100729",
• URL = "http://www.sciencedirect.com/science/article/pii/S2210650220303825",
• keywords = "genetic algorithms, genetic programming, Semantic approximation, Code bloat",
• abstract = "Code bloat is a phenomenon in Genetic Programming (GP) characterized by the increase in individual size during the evolutionary process without a corresponding improvement in fitness. Bloat negatively affects GP performance, since large individuals are more time consuming to evaluate and harder to interpret. In this paper, we propose two approaches for reducing GP code bloat based on a semantic approximation technique. The first approach replaces a random subtree in an individual by a smaller tree of approximate semantics. The second approach replaces a random subtree by a smaller tree that is semantically approximate to the desired semantics. We evaluated the proposed methods on a large number of regression problems. The experimental results showed that our methods help to significantly reduce code bloat and improve the performance of GP compared to standard GP and some recent bloat control methods in GP. Furthermore, the performance of the proposed approaches is competitive with the best machine learning technique among the four tested machine learning algorithms",

}

Genetic Programming entries for Quang Uy Nguyen Thi Huong Chu

Citations