Empirical Analysis of the Factors that A ect

The inclusion of learning in genetic algorithms based on the Baldwin eeect is one of the popular approaches to improving the convergence of genetic algorithms. However, the expected improvement may not be easily obtained. This is mainly due to the lack of understanding of the factors that aaect the Baldwin eeect. This paper aims at providing suucient evidence to connrm that the level of diiculties for genetic operations to produce the genotypic changes that match the phenotypic changes due to learning can signiicantly aaect the Baldwin eeect. The results suggest that combining genetic algorithms inattentively with any learning methods available is not a proper way to construct hybrid algorithms. Instead, the correlation between the genetic operations and the learning methods has to be carefully considered.