Direct memory schemes for population-based incremental learning in cyclically changing environments

Date

2016-04

Advisors

Journal Title

Journal ISSN

ISSN

0302-9743

Volume Title

Publisher

Springer

Type

Conference

Peer reviewed

Yes

Abstract

The population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. The integration of PBIL with associative memory schemes has been successfully applied to solve dynamic optimization problems (DOPs). The best sample together with its probability vector are stored and reused to generate the samples when an environmental change occurs. It is straight forward that these methods are suitable for dynamic environments that are guaranteed to reappear, known as cyclic DOPs. In this paper, direct memory schemes are integrated to the PBIL where only the sample is stored and reused directly to the current samples. Based on a series of cyclic dynamic test problems, experiments are conducted to compare PBILs with the two types of memory schemes. The experimental results show that one specific direct memory scheme, where memory-based immigrants are generated, always improves the performance of PBIL. Finally, the memory-based immigrant PBIL is compared with other peer algorithms and shows promising performance.

Description

The file attached to this record is the authors final peer reviewed version. The publisher's final version can be found by following the DOI link.

Keywords

Population-based incremental learning, Direct memory schemes, cyclically changing environments

Citation

Mavrovouniotis, M. and Yang, S. (2016) Direct memory schemes for population-based incremental learning in cyclically changing environments. EvoApplications 2016: Applications of Evolutionary Computation, 9598, pp. 233-247

Rights

Research Institute

Institute of Artificial Intelligence (IAI)