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Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

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Abstract

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the “evaporation concept” applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The “evaporation concept” is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

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Authors and Affiliations

  1. Department of Information Management, Yuan-Ze University, 135 Yuan Tung Road, Chung-Li, Taiwan, ROC, 32026

    Pei-Chann Chang

  2. Department of Electronic Commerce Management, Nanhua University, 32, Chungkeng, Dalin, Chiayi, 62248, Taiwan, ROC

    Shih-Hsin Chen

  3. Department of Industrial Engineering and Management, Yuan-Ze University, Chung-Li, Taiwan, ROC

    Chin-Yuan Fan

  4. Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India

    V. Mani

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  1. Pei-Chann Chang
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  2. Shih-Hsin Chen
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  4. V. Mani
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Correspondence to Pei-Chann Chang.

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Chang, PC., Chen, SH., Fan, CY. et al. Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems. Ann Oper Res 180, 197–211 (2010). https://doi.org/10.1007/s10479-008-0489-9

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