Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
| Hi-index | 0.00 |
We propose an extended co-evolutionary algorithm (CA) with probabilistic model building (CA-PMB) in order to improve the search performance of the CA. This article specifically describes an implementation of CA-PMB called a co-evolutionary algorithm with population-based incremental learning (CA-PBIL), and analyzes the behavior of the algorithm through computational experiments using an intransitive numbers game as a benchmark problem. The experimental results show that desirable co-evolution may be inhibited by the over-specialization effect, and that the algorithm shows complex dynamics caused by the game's intransitivity. However, further experiments show that the intransitivity encourages desirable co-evolution when a different learning rate is set for each population.