z-logo
open-access-imgOpen Access
Diversity Control in Evolutionary Computation using Asynchronous Dual-Populations with Search Space Partitioning
Author(s) -
Hassan A. Bashir
Publication year - 2020
Publication title -
nigerian journal of technological development
Language(s) - English
Resource type - Journals
ISSN - 2437-2110
DOI - 10.4314/njtd.v17i3.4
Subject(s) - evolutionary computation , mathematical optimization , computer science , evolutionary algorithm , heuristic , convergence (economics) , population , machine learning , mathematics , demography , sociology , economics , economic growth
Diversity control is vital for effective global optimization using evolutionary computation (EC) techniques. This paper classifies the various diversity control policies in the EC literature. Many research works have attributed the high risk of premature convergence to sub-optimal solutions to the poor exploration capabilities resulting from diversity collapse. Also, excessive cost of convergence to optimal solution has been linked to the poor exploitation capabilities necessary to focus the search. To address this exploration-exploitation trade-off, this paper deploys diversity control policies that ensure sustained exploration of the search space without compromising effective exploitation of its promising regions. First, a dual-pool EC algorithm that facilitates a temporal evolution-diversification strategy is proposed. Then a quasi-random heuristic initialisation based on search space partitioning (SSP) is introduced to ensure uniform sampling of the initial search space. Second, for the diversity measurement, a robust convergence detection mechanism that combines a spatial diversity measure; and a population evolvability measure is utilised. It was found that the proposed algorithm needed a pool size of only 50 samples to converge to optimal solutions of a variety of global optimization benchmarks. Overall, the proposed algorithm yields a 33.34% reduction in the cost incurred by a standard EC algorithm. The outcome justifies the efficacy of effective diversity control on solving complex global optimization landscapes. Keywords: Diversity, exploration-exploitation tradeoff, evolutionary algorithms, heuristic initialisation, taxonomy.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here