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Solving inverse problems by the multi-deme hierarchic genetic strategy
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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On Stability and Classification Tools for Genetic Algorithms
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Evolutionary multiobjective optimization algorithm as a Markov system
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Bio-inspired algorithms for autonomous deployment and localization of sensor nodes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume Part I
On Stability and Classification Tools for Genetic Algorithms
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
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Evolutionary Computation
Multiobjective evolutionary strategy for finding neighbourhoods of pareto-optimal solutions
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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This book is devoted to the application of genetic algorithms in continuous global optimization. Some of their properties and behavior are highlighted and formally justified. Various optimization techniques and their taxonomy are the background for detailed discussion. The nature of continuous genetic search is explained by studying the dynamics of probabilistic measure, which is utilized to create subsequent populations. This approach shows that genetic algorithms can be used to extract some areas of the search domain more effectively than to find isolated local minima. The biological metaphor of such behavior is the whole population surviving by rapid exploration of new regions of feeding rather than caring for a single individual. One group of strategies that can make use of this property are two-phase global optimization methods. In the first phase the central parts of the basins of attraction are distinguished by genetic population analysis. Afterwards, the minimizers are found by convex optimization methods executed in parallel.