Abstract
Learnable evolution model (LEM) is an efficient evolutionary algorithm for function optimization proposed by Michalski. However, because the design of LEM is for continuous optimization, how to apply it to discrete optimization has become an important issue. In this paper, we present a discrete version of LEM for discrete optimization of traveling salesman problem (TSP), called LEMTSP. The proposed algorithm makes LEM capable of solving problems in the discrete domain while preserving its convergence speed. More important, the proposed algorithm can use all the crossover operators designed particularly for discrete problems.
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Wu, W., Tseng, SP. (2017). An Improved Learnable Evolution Model for Discrete Optimization Problem. In: Pan, JS., Tsai, PW., Huang, HC. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 64. Springer, Cham. https://doi.org/10.1007/978-3-319-50212-0_40
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DOI: https://doi.org/10.1007/978-3-319-50212-0_40
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