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Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An analysis of the effects of selection in genetic algorithms
An analysis of the effects of selection in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
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Proceedings of the 5th International Conference on Genetic Algorithms
A New Genetic Algorithm Using Large Mutation Rates and Population-Elitist Selection (GALME)
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Expert Systems with Applications: An International Journal
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new geometric shape-based genetic clustering algorithm for the multi-depot vehicle routing problem
Expert Systems with Applications: An International Journal
Application of Genetic Algorithm in unit selection for Malay speech synthesis system
Expert Systems with Applications: An International Journal
Mathematical and Computer Modelling: An International Journal
Expert Systems with Applications: An International Journal
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The applications of genetic algorithms (GAs) in solving combinatorial problems are frequently faced with a problem of early convergence and the evolutionary processes are often trapped in a local optimum. This premature convergence occurs when the population of a genetic algorithm reaches a suboptimal state that the genetic operators can no longer produce offspring with a better performance than their parents. In the literature, plenty of work has been investigated to introduce new methods and operators in order to overcome this essential problem of genetic algorithms. As these methods and the belonging operators are rather problem specific in general. In this research, we take a different approach by constantly observing the progress of the evolutionary process and when the diversity of the population dropping below a threshold level then artificial chromosomes with high diversity will be introduced to increase the average diversity level thus to ensure the process can jump out the local optimum and to revolve again. A dynamic threshold control mechanism is built up during the evolutionary process to further improve the system performance. The proposed method is implemented independently of the problem characteristics and can be applied to improve the global convergence behavior of genetic algorithms. The experimental results using TSP instances show that the proposed approach is very effective in preventing the premature convergence when compared with other approaches.