NDLI: Memetic informed evolutionary optimization via data mining

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Memetic informed evolutionary optimization via data mining

Content Provider	Springer Nature Link
Author	Chia, J. Y. Goh, C. K. Tan, K. C. Shim, V. A.
Copyright Year	2011
Abstract	This paper proposes a novel Informed Evolutionary algorithm (InEA) which implements the idea of learning with a generation. An association rule miner is used to identify the norm of a population. Subsequently, a knowledge based mutation operator is used to help guide the search of the evolutionary optimizer. The approach breaks away from the current practice of treating the optimization and analysis process as two independent processes. It shows how a rule mining module can be used to mine knowledge and hybridized into EA to improve the performance of the optimizer. The proposed memetic algorithm is examined via various benchmarks problems, and the simulation results show that InEA is competitive as compared to existing approaches in literature.
Starting Page	73
Ending Page	87
Page Count	15
File Format	PDF
ISSN	18659284
Journal	Memetic Computing
Volume Number	3
Issue Number	2
e-ISSN	18659292
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-06-04
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data mining Evolutionary algorithm Memetic algorithm Single objective optimization Bioinformatics Control, Robotics, Mechatronics Artificial Intelligence (incl. Robotics) ApplicationMathematics/Computational Methods of Engineering Applications of Mathematics Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Science

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