A behavioral approach of the dynamics of financial markets
Proceedings of the conference on First specialized conference on decision support systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Game-Theoretic Approach to the Simple Coevolutionary Algorithm
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Genetic algorithms in a competitive environment with application to reliability optimal design
ICAAICSE '01 Proceedings of the sixth international conference on Application of artificial intelligence to civil & structural engineering
Optimum topological design of simply supported composite stiffened panels via genetic algorithms
Computers and Structures
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Competition is introduced among the populations of a number of genetic algorithms (GAs) having different sets of parameters. The aim is to calibrate the population size of the GAs by altering the resources of the system, i.e. the allocated computing time. The co-evolution of the different populations is controlled at the level of the union of populations, i.e. the metapopulation, on the basis of statistics and trends of the evolution of every population. Evolution dynamics improve the capacity of the optimization algorithm to find optimum solutions and results in statistically better designs as compared to the standard GA with any of the fixed parameters considered. The method is applied to the reliability based optimal design of simple trusses. Numerical results are presented and the robustness of the proposed algorithm is discussed.