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

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 (EA...

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Bibliographic Details
Published in:Annals of operations research Vol. 180; no. 1; pp. 197 - 211
Main Authors: Chang, Pei-Chann, Chen, Shih-Hsin, Fan, Chin-Yuan, Mani, V.
Format: Journal Article
Language:English
Published: Boston Springer US 01.11.2010
Springer Science + Business Media
Springer
Springer Nature B.V
Subjects:
Algorithms
Analysis
Artificial chromosomes
Business and Management
Combinatorics
Electromagnetism
Evaporation
Genes
Genetic algorithms
Genetics
Heuristic
Job shops
Literature reviews
Mathematical models
Operations research
Operations Research/Decision Theory
Optimization
Permutations
Production scheduling
Scheduling
Studies
Theory of Computation
Traveling salesman problem
Iran
Total deviations
Flowshop machine scheduling
Evolutionary algorithm with probabilistic models
Artificial chromosomes
Single machine scheduling
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary: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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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-008-0489-9