The inclusion of learning in genetic algorithms based on the Baldwin effect 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 affect the Baldwin effect. This paper aims at providing sufficient evidence to confirm that the level of difficulties for genetic operations to produce the genotypic changes that match the phenotypic changes due to learning can significantly affect the Baldwin effect. 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.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||5th International Conference on Parallel Problem Solving from Nature, PPSN 1998|
|Period||27/09/98 → 30/09/98|
- Theoretical Computer Science
- Computer Science(all)