NDLI: When and what kind of memetic algorithms perform well

Content Provider	IEEE Xplore Digital Library
Author	Jih-Yiing Lin Ying-ping Chen
Copyright Year	2012
Description	Author affiliation: Department of Computer Science, National Chiao Tung University, HsinChu City, TAIWAN (Jih-Yiing Lin; Ying-ping Chen)
Abstract	The synergy between exploration and exploitation has been a prominent issue in optimization. The rise of memetic algorithms, a category of optimization techniques which feature the explicit exploration-exploitation coordination, much accentuates this issue. While memetic algorithms have achieved remarkable success in a wide range of real-world applications, the key to a successful exploration-exploitation synergy still remains obscure. Manifold empirical results and theoretical derivations have been proposed and provided various perspectives from different algorithm-problem complexes to this issue. In our previous work, the concept of local search zones was proposed to provide an alternative perspective depicting the general behavior of memetic algorithms on a broad range of problems. In this work, based on the local search zone concept, we further investigate how the problem landscape and the way the algorithm explores and exploits the search space affect the performance of a memetic algorithm. The collaborative behavior of several representative archetypes of memetic algorithms, which exhibit different degrees of explorability and exploitability, are illustrated empirically and analytically on problems with different landscapes. As the empirical results consist with the local search zone concept and describe the behavior of various memetic algorithms on different problems, this work may reveal some essential design principals for memetic algorithms.
Starting Page	1
Ending Page	8
File Size	814247
Page Count	8
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6252894
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Memetics Search problems Optimization Algorithm design and analysis Heuristic algorithms Collaboration Space exploration
Content Type	Text
Resource Type	Article

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