Hyper-learning for population-based incremental learning in dynamic environments.

Date

2009

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE

Type

Article

Peer reviewed

Yes

Abstract

The population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. Recently, the PBIL algorithm has been applied for dynamic optimization problems. This paper investigates the effect of the learning rate, which is a key parameter of PBIL, on the performance of PBIL in dynamic environments. A hyper-learning scheme is proposed for PBIL, where the learning rate is temporarily raised whenever the environment changes. The hyper-learning scheme can be combined with other approaches, e.g., the restart and hypermutation schemes, for PBIL in dynamic environments. Based on a series of dynamic test problems, experiments are carried out to investigate the effect of different learning rates and the proposed hyper-learning scheme in combination with restart and hypermutation schemes on the performance of PBIL. The experimental results show that the learning rate has a significant impact on the performance of the PBIL algorithm in dynamic environments and that the effect of the proposed hyper-learning scheme depends on the environmental dynamics and other schemes combined in the PBIL algorithm.

Description

Keywords

Evolutionary computation, Learning (artificial intelligence), Optimisation, Dynamic optimization problems, Hyper-learning scheme, Hypermutation schemes, Population-based incremental learning algorithm

Citation

Yang, L. and Richter, H. (2009) Hyper-learning for population-based incremental learning in dynamic environments . In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, Trondheim, 2009. New York: IEEE, pp. 682-689.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)