Estimation of distribution algorithm based on probabilistic grammar with latent annotations

Abstract

Evolutionary algorithms (EAs) are optimization methods and are based on the concept of natural evolution. Recently, growing interests has been observed on applying estimation of distribution techniques to EAs (EDAs). Although probabilistic context free grammar (PCFG) is a widely used model in EDAs for program evolution, it is not able to estimate the building blocks from promising solutions because it takes advantage of the context freedom assumption. We have proposed a new program evolution algorithm based on PCFG with latent annotations which weaken the context freedom assumption. Computational experiments on two subjects (the royal tree problem and the DMAX problem) demonstrate that our new approach is highly effective compared to prior approaches including the conventional GP.