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

Published in Conferences Papers
  1. Edgar Galván López, Brendan Cody-Kenny, Leonardo Trujillo and Ahmed Kattan. Using semantics in the selection mechanism in Genetic Programming: A simple method for promoting semantic diversity. In IEEE Congress on Evolutionary Computation. 2013, 2972-2979. BibTeX

    @inproceedings{DBLP:conf/cec/LopezCTK13,
    	author = "Edgar Galv{\'a}n L{\'o}pez and Brendan Cody-Kenny and Leonardo Trujillo and Ahmed Kattan",
    	title = "Using semantics in the selection mechanism in Genetic Programming: A simple method for promoting semantic diversity",
    	booktitle = "IEEE Congress on Evolutionary Computation",
    	year = 2013,
    	pages = "2972-2979",
    	ee = "http://dx.doi.org/10.1109/CEC.2013.6557931",
    	crossref = "DBLP:conf/cec/2013",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
  2. Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2013, Cancun, Mexico, June 20-23, 2013. IEEE, 2013. BibTeX

    @proceedings{DBLP:conf/cec/2013,
    	title = "Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2013, Cancun, Mexico, June 20-23, 2013",
    	booktitle = "IEEE Congress on Evolutionary Computation",
    	publisher = "IEEE",
    	year = 2013,
    	isbn = "978-1-4799-0452-5, 978-1-4799-0453-2",
    	ee = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6552460",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
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 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
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
20-23 June 2013
E-ISBN
978-1-4799-0452-5
ISBN
978-1-4799-0453-2
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