Please use this identifier to cite or link to this item: https://doi.org/10.1007/s12293-011-0058-7
Title: Memetic informed evolutionary optimization via data mining
Authors: Chia, J.Y.
Goh, C.K.
Tan, K.C. 
Shim, V.A.
Keywords: Data mining
Evolutionary algorithm
Memetic algorithm
Single objective optimization
Issue Date: Jul-2011
Source: Chia, J.Y.,Goh, C.K.,Tan, K.C.,Shim, V.A. (2011-07). Memetic informed evolutionary optimization via data mining. Memetic Computing 3 (2) : 73-87. ScholarBank@NUS Repository. https://doi.org/10.1007/s12293-011-0058-7
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. © 2011 Springer-Verlag.
Source Title: Memetic Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/56602
ISSN: 18659284
DOI: 10.1007/s12293-011-0058-7
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