An analysis on separability for Memetic Computing automatic design
File(s)
Accepted version
Author(s)
Caraffini, Fabio
Neri, Ferrante
Picinali, Lorenzo
Type
Journal Article
Abstract
This paper proposes a computational prototype for automatic design of optimization algorithms. The proposed scheme makes an analysis of the problem that estimates the degree of separability of the optimization problem. The separability is estimated by computing the Pearson correlation indices between pairs of variables. These indices are then manipulated to generate a unique index that estimates the separability of the entire problem. The separability analysis is thus used to design the optimization algorithm that addresses the needs of the problem. This prototype makes use of two operators arranged in a Parallel Memetic Structure. The first operator performs moves along the axes while the second simultaneously perturbs all the variables to follow the gradient of the fitness landscape. The resulting algorithmic implementation, namely Separability Prototype for Automatic Memes (SPAM), has been tested on multiple testbeds and various dimensionality levels. The proposed computational prototype proved to be a flexible and intelligent framework capable to learn from a problem and, thanks to this learning, to outperform modern meta-heuristics representing the-state-of-the-art in optimization.
Date Issued
2014-05-01
Date Acceptance
2013-12-29
Citation
Information Sciences, 2014, 265 (1), pp.1-22
ISSN
0020-0255
Publisher
Elsevier
Start Page
1
End Page
22
Journal / Book Title
Information Sciences
Volume
265
Issue
1
Copyright Statement
© 2014 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000333502600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Computer Science, Information Systems
Computer Science
Memetic Computing
Optimization algorithm
Algorithmic design
Computational intelligence optimization
PARTICLE SWARM OPTIMIZATION
DIFFERENTIAL EVOLUTION
HYPER-HEURISTICS
GLOBAL OPTIMIZATION
LOCAL SEARCH
ALGORITHM
ADAPTATION
SELECTION
STRATEGY
Publication Status
Published
Date Publish Online
2014-01-07