Genetic algorithms for flowshop scheduling problems
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Parallel machine scheduling with earliness and tardiness penalties
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability; A Guide to the Theory of NP-Completeness
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Scheduling of drilling operations in printed circuit board factory
Computers and Industrial Engineering
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Parametric Study To Enhance The Genetic Algorithm's Performance When Using Transformation
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Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Expert Systems with Applications: An International Journal
A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem
Expert Systems with Applications: An International Journal
Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Optimizing patrol force deployment using a genetic algorithm
Expert Systems with Applications: An International Journal
Quantum genetic algorithm for hybrid flow shop scheduling problems to minimize total completion time
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
FMS scheduling with knowledge based genetic algorithm approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Genetic regulatory network-based symbiotic evolution
Expert Systems with Applications: An International Journal
Non-identical parallel machine scheduling using genetic algorithm
Expert Systems with Applications: An International Journal
A two-stage hybrid memetic algorithm for multiobjective job shop scheduling
Expert Systems with Applications: An International Journal
A multiobjective optimization approach to solve a parallel machines scheduling problem
Advances in Artificial Intelligence
Advances in Engineering Software
Multi-objective genetic-based algorithms for a cross-docking scheduling problem
Applied Soft Computing
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Variable neighborhood search for drilling operation scheduling in PCB industries
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Hi-index | 12.07 |
This paper introduces a two@?phase sub population genetic algorithm to solve the parallel machine-scheduling problem. In the first phase, the population will be decomposed into many sub-populations and each sub-population is designed for a scalar multi-objective. Sub-population is a new approach for solving multi-objective problems by fixing each sub-population for a pre-determined criterion. In the second phase, non-dominant solutions will be combined after the first phase and all sub-population will be unified as one big population. Not only the algorithm merges sub-populations but the external memory of Pareto solution is also merged and updated. Then, one unified population with each chromosome search for a specific weighted objective during the next evolution process. The two phase sub-population genetic algorithm is applied to solve the parallel machine-scheduling problems in testing of the efficiency and efficacy. Experimental results are reported and the superiority of this approach is discussed.