Learning Genetic Representations for Classes of Real-Valued Optimization Problems

Abstract

Applying evolutionary algorithms to new problem domains is an exercise in the art of parameter tuning and design decisions. A great deal of work has investigated ways to automate the tuning of various EA parameters such as population size, mutation options, etc. However, genotypeto-phenotype mappings have typically been considered too complex to adapt automatically. We demonstrate a genetic representation learning method that uses meta-evolution to adapt a bitstring encoding for a synthetic class of real-valued optimization problems. The genetic representation we learn performs as well or better than a Gray code both on new instances of the problem class it was trained on and on problem types that it was not trained on.

Publication
In Genetic and Evolutionary Computation
Date
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