Cost-benefit Analysis of Using Heuristics in ACGP

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Aleshunas:2011:CAoUHiA,

• title = "Cost-benefit Analysis of Using Heuristics in {ACGP}",
• author = "John Aleshunas and Cezary Janikow",
• pages = "1177--1183",
• booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation",
• year = "2011",
• editor = "Alice E. Smith",
• month = "5-8 " # jun,
• address = "New Orleans, USA",
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CEC.2011.5949749",
• abstract = "Constrained Genetic Programming (CGP) is a method of searching the Genetic Programming search space non-uniformly, giving preferences to certain subspaces according to some heuristics. Adaptable CGP (ACGP) is a method for discovery of the heuristics. CGP and ACGP have previously demonstrated their capabilities using first-order heuristics: parent-child probabilities. Recently, the same advantage has been shown for second-order heuristics: parent- children probabilities. A natural question to ask is whether we can benefit from extending ACGP with deeper-order heuristics. This paper attempts to answer this question by performing cost-benefit analysis while simulating the higher- order heuristics environment. We show that this method cannot be extended beyond the current second or possibly third-order heuristics without a new method to deal with the sheer number of such deeper-order heuristics.",
• notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.",

}

Genetic Programming entries for John Aleshunas Cezary Z Janikow

Citations