Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms

ABSTRACT Previously we demonstrated that Gray code is superior to binary code for genetic search in domains with ordered parameters. Since then we have determined that Gray code is better because it does not exhibit a counter-productive hidden bias that emerges when binary coding is used with the mutation search operator. But analysis suggests that crossover, the genetic algorithm's (GA) other search operator, should perform better with the binary representation. We present experimental results that show that genetic search using a multiple representation – Gray-coded mutation and binary-coded crossover – outperforms search using just one representation. We believe other search methods that use multiple search heuristics may also benefit from using multiple representations, one tuned for each heuristic.