Building on Success in Genetic Programming: Adaptive Variation and Developmental Evaluation

We investigate a developmental tree-adjoining grammar guided genetic programming system (DTAG3P+), in which genetic operator application rates are adapted during evolution. We previously showed developmental evaluation could promote structured solutions and improve performance in symbolic regression problems. However testing on parity problems revealed an unanticipated problem, that good building blocks for early developmental stages might be lost in later stages of evolution. The adaptive variation rate in DTAG3P+ preserves good building blocks found in early search for later stages. It gives both good performance on small k-parity problems, and good scaling to large problems.

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