Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Comparative Study of Steady State and Generational Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Effects of String Length and Mutation Rate on Success Probability of Genetic Algorithm
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 04
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An Adaptive Genetic Algorithm Based on Population Diversity Strategy
WGEC '09 Proceedings of the 2009 Third International Conference on Genetic and Evolutionary Computing
Dominance-Based Multiobjective Simulated Annealing
IEEE Transactions on Evolutionary Computation
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In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimise the number of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as simulated annealing and variable neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, genetic algorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a controlled migration operator into the GA heuristic, data, which represents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the-loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.