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Using semantics in the selection mechanism in genetic programming: a simple method for promoting semantic diversity
Galván-López, Edgar; Cody-Kenny, Brendan; Trujillo, Leonardo; Kattan, Ahmed
Research on semantics in Genetic Programming (GP) has increased over the last number of years. Results in this area clearly indicate that its use in GP considerably increases performance. Many of these semantic-based approaches rely on a trial-and-error method that attempts to find offspring that are semantically different from their parents over a number of trials using the crossover operator (crossover-semantics based - CSB). This, in consequence, has a major drawback: these methods could evaluate thousands of nodes, resulting in paying a high computational cost, while attempting to improve performance by promoting semantic diversity. In this work, we propose a simple and computationally inexpensive method, named semantics in selection, that eliminates the computational cost observed in CSB approaches. We tested this approach in 14 GP problems, including continuous- and discrete-valued fitness functions, and compared it against a traditional GP and a CSB approach. Our results are equivalent, and in some cases, superior than those found by the CSB approach, without the necessity of using a “brute force” mechanism.
Keyword(s): computational efficiency; context; genetic programming; semantics
Publication Date:
Type: Conference item
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Funder(s): Science Foundation Ireland
Citation(s): 2013 IEEE Congress on Evolutionary Computation;pp. 2972-2979
Publisher(s): IEEE Computer Society
First Indexed: 2014-09-25 05:37:36 Last Updated: 2019-09-19 06:26:06