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C. FERREIRA, 2002 (Terms of Use) ISBN: 9729589054

Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence

Genetic operators and their power
 

In this section:


Everybody agrees that, by and large, evolution relies on genetic variation coupled with some kind of selection and, in fact, all evolutionary algorithms explore these fundamental processes. However, there is no agreement concerning the best way to create genetic variation, with researchers divided between mutation and recombination. This fact per se is extremely revealing, suggesting that existing artificial evolutionary systems are fundamentally different from one another. Indeed, artificial evolutionary systems are themselves still evolving and among them, camouflaged by different representations, can be found simple replicator systems, rudimentary genotype/phenotype systems, and full-fledged genotype/phenotype systems. And the mechanisms of genetic modification of all systems are intricately connected with their representation schemes.

We have already seen that gene expression programming uses not only mutation and recombination but also different kinds of transposition and, therefore, can be useful for conducting a rigorous analysis of the power of different search operators in order to gain some insights into their role in evolution. We will see that mutation is by far the single most important genetic operator, outperforming recombination considerably. In fact, all three kinds of genetic recombination analyzed here (one-point, two-point, and gene recombination) perform considerably worse than mutation and also considerably worse than simple intrachromosomal transposition mechanisms. In addition, we are also going to analyze with great detail the evolutionary dynamics produced by all these genetic operators in order to understand their importance in evolution.

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