Improved Adaptive Genetic Algorithms for Job Shop Scheduling Problems

Mei Hong Liu; Xiong Feng Peng

doi:10.4028/www.scientific.net/AMR.97-101.2473

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 97-101Improved Adaptive Genetic Algorithms for Job Shop...

Improved Adaptive Genetic Algorithms for Job Shop Scheduling Problems

Abstract:

In this paper, the adaptability of the genetic algorithm (GA) is considered. Two improved adaptive genetic algorithms (AGA) which are called Ch-AGA and Th-AGA for short are proposed based on the previous AGA. The crossover probability and the mutation probability of the Ch-AGA and the Th-AGA are non-linear changed between some a certain region, and adopted the mathematical function of chx and thx respectively. The two improved adaptive genetic algorithms are used to solve the classical job shop scheduling problems and the results indicate that the algorithms are more effective and more efficient than previous AGA, and should be used in practical applications.

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Periodical:

Advanced Materials Research (Volumes 97-101)

Pages:

2473-2476

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.97-101.2473

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Online since:

March 2010

Authors:

Mei Hong Liu, Xiong Feng Peng

Keywords:

Adaptive Genetic Algorithm (AGA), Crossover Probability, Mutation Probability

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