Learning Genetic Representations for Classes of Real-Valued Optimization Problems
Eric O. Scott, Jeffrey K. Bassett · 2015
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, genotype-to-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.