A Self-Selecting Crossover Operator

This paper compares the efficacy of different crossover operators for Grammatical Evolution across a typical numeric regression problem and a typical data classification problem. Grammatical evolution is an extension of genetic programming, in that it is an algorithm for evolving complete programs in an arbitrary language. Each of the two main crossover operators struggles (for different reasons) to achieve 100% correct solutions. A mechanism is proposed, allowing the evolutionary algorithm to self-select the type of crossover utilised and this is shown to improve the rate of generating 100% successful solutions.

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