Using Semantics in the Selection Mechanism in Genetic Programming: a Simple Method for Promoting Semantic Diversity

Published in Conferences Papers
  1. Y Martínez, E Naredo, L Trujillo and E Galván-López. Searching for novel regression functions. In 2013 IEEE Congress on Evolutionary Computation (). June 2013, 16-23. DOI BibTeX

    	author = "Y. Martínez and E. Naredo and L. Trujillo and E. Galván-López",
    	booktitle = "2013 IEEE Congress on Evolutionary Computation",
    	title = "Searching for novel regression functions",
    	year = 2013,
    	volume = "",
    	number = "",
    	pages = "16-23",
    	keywords = "genetic algorithms;regression analysis;search problems;NS;behavior-based search;domain-specific descriptor;evolutionary computation;genetic programming;novelty search algorithm;regression functions;semantics-based GP algorithms;symbolic regression;Context;Robots;Search problems;Semantics;Sociology;Statistics;Vectors;Behavior-based Search;Genetic Programming;Novelty Search;Symbolic Regression",
    	doi = "10.1109/CEC.2013.6557548",
    	issn = "1089-778X",
    	month = "June"

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.

Published in
IEEE Congress on Evolutionary Computation (CEC)
Pages 2972 - 2979
Date of conference
20-23 June 2013