Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Global optimization
Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems
Fuzzy Sets and Systems - Special issue on operations research
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
Genetic algorithm parameter sets for line labelling
Pattern Recognition Letters - special issue on pattern recognition in practice V
Simple genetic algorithm with local tuning: efficient global optimizing technique
Journal of Optimization Theory and Applications
Improved genetic operators for structural engineering optimization
Advances in Engineering Software
Computers and Operations Research
Analysis of speciation and niching in the multi-niche crowding GA
Theoretical Computer Science - Special issue on evolutionary computation
Theoretical Computer Science
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A multipopulation genetic algorithm aimed at multimodal optimization
Advances in Engineering Software
Hybrid simplex genetic algorithm for blind equalization using RBF networks
Mathematics and Computers in Simulation
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Niche identification techniques in multimodal genetic search with sharing scheme
Advances in Engineering Software
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem
Expert Systems with Applications: An International Journal
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
A hybrid real-parameter genetic algorithm for function optimization
Advanced Engineering Informatics
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A hybrid approach to modeling metabolic systems using a geneticalgorithm and simplex method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simulated annealing: Practice versus theory
Mathematical and Computer Modelling: An International Journal
A fuzzy clustering-based niching approach to multimodal function optimization
Cognitive Systems Research
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The hybridization of genetic algorithms and the simplex method have been proven in literature as useful and promising in optimizations. Therefore, this paper proposes a multi-teams genetic-algorithm (MT-GA) hybrid developed toward extending the previous simplex-GA hybrids. The approach utilizes the simplex method as a united team and multi-teams collaboration and also competition search process in conjunction with the GAs. It is designed such that it has multi-teams with self-evolution (parallel applications of the simplex method), multi-teams communication and even mutual stimulation, and multi-teams survival competition as well as non-elite team breakup for individual relearning (with GAs) and re-forming the new teams. The extension of multi-teams GA thus provides the advantages and as previous simplex-GAs has been proved to outperform a number of other approaches. The experiments in this research show that the MT-GA generally outperforms the existing simplex-GAs for the indices of convergence rate (CPU time required), efficiency (number of function evaluations), and effectiveness (accuracy). Also, a further functional experiment of the MT-GA shows that the MT-GA can be a useful improved algorithm for the function optimization problems.