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Memetic informed evolutionary optimization via data mining

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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.

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Authors and Affiliations

  1. National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore

    J. Y. Chia, K. C. Tan & V. A. Shim

  2. Rolls-Royce Singapore Pte Ltd, 50 Nanyang Avenue, Singapore, 639798, Singapore

    C. K. Goh

Authors
  1. J. Y. Chia
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  2. C. K. Goh
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  3. K. C. Tan
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  4. V. A. Shim
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Correspondence to V. A. Shim.

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Chia, J.Y., Goh, C.K., Tan, K.C. et al. Memetic informed evolutionary optimization via data mining. Memetic Comp. 3, 73–87 (2011). https://doi.org/10.1007/s12293-011-0058-7

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  • DOI: https://doi.org/10.1007/s12293-011-0058-7

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