Catalog Home Page

Classification of adaptive memetic algorithms: A comparative study

Ong, Y.S., Lim, M.H., Zhu, N. and Wong, K.W. (2006) Classification of adaptive memetic algorithms: A comparative study. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 36 (1). pp. 141-152.

[img]
Preview
PDF - Published Version
Download (580kB) | Preview
    Link to Published Version: http://dx.doi.org/10.1109/TSMCB.2005.856143
    *Subscription may be required

    Abstract

    Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

    Publication Type: Journal Article
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: © 2006 IEEE.
    Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    URI: http://researchrepository.murdoch.edu.au/id/eprint/982
    Item Control Page

    Downloads

    Downloads per month over past year