An agent-based model of hierarchic genetic search

Authored by Robert Schaefer, Aleksander Byrski, Joanna Kolodziej, Maciej Smolka

Date Published: 2012-12

DOI: 10.1016/j.camwa.2012.02.052

Sponsors: Polish Ministry of Science and Higher Education

Platforms: No platforms listed

Model Documentation: Other Narrative Pseudocode Mathematical description

Model Code URLs: Model code not found

Abstract

An effective exploration of the large search space by single population genetic-based metaheuristics may be a very time consuming and complex process, especially in the case of dynamic changes in the system states. Speeding up the search process by the metaheuristic parallelisation must have a significant negative impact on the search accuracy. There is still a lack of complete formal models for parallel genetic and evolutionary techniques, which might support the parameter setting and improve the whole (often very complex) structure management. In this paper, we define a mathematical model of Hierarchical Genetic Search (HGS) based on the genetic multi-agent system paradigm. The model has a decentralised population management mechanism and the relationship among the parallel genetic processes has a multi-level tree structure. Each process in this tree is Markov-type and the conditions of the commutation of the Markovian kernels in HGS branches are formulated. (C) 2012 Elsevier Ltd. All rights reserved.

Tags

Genetic Algorithms Global optimisation Hierarchic genetic strategy Multi-agent genetic system