All binary representations are equal: but some are more equal than others

Abstract

The original demonstration by G. Hinton and S. Nowlan (1987) of the Baldwin effect (J. Baldwin, 1896) is well-known and serves as an interesting basis for genetic algorithm (GA) research. A variant of the original representation used a binary code, in which learning was expressed as a substitute for internalised knowledge; in this paper, the representation is altered such that learning becomes an expression of uncertainty. This change results in an interesting and non-trivial set of interactions between the GA operators and the representation, as well as enhancing the performance and robustness of the GA.