Using Semantics in the Selection Mechanism in Genetic Programming: a Simple Method for Promoting Semantic Diversity
Y Martínez, E Naredo, L Trujillo and E GalvánLópez. Searching for novel regression functions. In 2013 IEEE Congress on Evolutionary Computation (). June 2013, 1623. DOI BibTeX
@inproceedings{6557548, author = "Y. Martínez and E. Naredo and L. Trujillo and E. GalvánLópez", booktitle = "2013 IEEE Congress on Evolutionary Computation", title = "Searching for novel regression functions", year = 2013, volume = "", number = "", pages = "1623", keywords = "genetic algorithms;regression analysis;search problems;NS;behaviorbased search;domainspecific descriptor;evolutionary computation;genetic programming;novelty search algorithm;regression functions;semanticsbased GP algorithms;symbolic regression;Context;Robots;Search problems;Semantics;Sociology;Statistics;Vectors;Behaviorbased Search;Genetic Programming;Novelty Search;Symbolic Regression", doi = "10.1109/CEC.2013.6557548", issn = "1089778X", month = "June" }
 Abstract

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 semanticbased approaches rely on a trialanderror method that attempts to find offspring that are semantically different from their parents over a number of trials using the crossover operator (crossoversemantics 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 discretevalued 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
 http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6557931&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6557931
 Date of conference
 2023 June 2013
 EISBN
 9781479904525
 ISBN
 9781479904532