Cost-benefit analysis of using heuristics in ACGP

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Cost-benefit analysis of using heuristics in ACGP
—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 deeperorder 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. Keywords-Genetic Programming, Adaptable Constrained Genetic Programming, Building Block Hypothe...
John W. Aleshunas, Cezary Z. Janikow
Added 13 Dec 2011
Updated 13 Dec 2011
Type Journal
Year 2011
Where CEC
Authors John W. Aleshunas, Cezary Z. Janikow
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