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Conceptual modeling of evolvable local searches in memetic algorithms using linear genetic programming: a case study on capacitated vehicle routing problem

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Abstract

This paper presents a study on the conceptual modeling of memetic algorithm with evolvable local search in the form of linear programs, self-assembled by linear genetic programming based evolution. In particular, the linear program structure for local search and the associated local search self-assembling process in the lifetime learning process of memetic algorithm are proposed. Results showed that the memetic algorithm with evolvable local search provides a means of creating highly robust, self-configuring and scalable algorithms, thus generating improved or competitive results when benchmarking against several existing adaptive or human-designed state-of-the-art memetic algorithms and meta-heuristics, on a plethora of capacitated vehicle routing problem sets considered.

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Acknowledgments

This work is partially supported under the A\(^*\)Star-TSRP funding, Singapore Institute of Manufacturing Technology-Nanyang Technological University (SIMTech-NTU) Joint Laboratory and Collaborative research Programme on Complex Systems, the Computational Intelligence Research Laboratory at NTU.

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Correspondence to Liang Feng.

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Communicated by V. Loia.

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Feng, L., Ong, YS., Chen, C. et al. Conceptual modeling of evolvable local searches in memetic algorithms using linear genetic programming: a case study on capacitated vehicle routing problem. Soft Comput 20, 3745–3769 (2016). https://doi.org/10.1007/s00500-015-1971-3

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