Integer and combinatorial optimization
Integer and combinatorial optimization
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Annals of Operations Research - Special issue on Tabu search
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Genetic algorithm crossover operators for ordering applications
Computers and Operations Research - Special issue on genetic algorithms
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
A genetic algorithm for the generalised assignment problem
Computers and Operations Research
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Genetic Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Lamarckian Evolution, The Baldwin Effect and Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Engineering Applications of Artificial Intelligence
Robotics and Computer-Integrated Manufacturing
Engineering Applications of Artificial Intelligence
A hybrid grouping genetic algorithm for reviewer group construction problem
Expert Systems with Applications: An International Journal
Unidirectional loop layout problem with balanced flow
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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Permutation property has been recognized as a common but challenging feature in combinatorial problems. Because of their complexity, recent research has turned to genetic algorithms to address such problems. Although genetic algorithms have been proven to facilitate the entire space search, they lack in fine-tuning capability for obtaining the global optimum. Therefore, in this study a hybrid genetic algorithm was developed by integrating both the evolutional and the neighborhood search for permutation optimization. Experimental results of a production scheduling problem indicate that the hybrid genetic algorithm outperforms the other methods, in particular for larger problems. Numerical evidence also shows that different input data from the initial, transient and steady states influence computation efficiency in different ways. Therefore, their properties have been investigated to facilitate the measure of the performance and the estimation of the accuracy.