Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Memory-based immigrants for genetic algorithms in dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A comparative study of immune system based genetic algorithms in dynamic environments
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Associative memory scheme for genetic algorithms in dynamic environments
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
A study on population's diversity for dynamic environments
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Solving dynamic constrained optimization problems with asynchronous change pattern
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
CHC-based algorithms for the dynamic traveling salesman problem
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Memory design for constrained dynamic optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Virtual loser genetic algorithm for dynamic environments
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Enhancing the virtual loser genetic algorithm for dynamic environments
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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When dealing with dynamic environments two major aspects must be considered in order to improve the algorithms' adaptability to changes: diversity and memory. In this paper we propose and study a new evolutionary algorithm that combines two populations, one playing the role of memory, with a biological inspired recombination operator to promote and maintain diversity. The size of the memory mechanism may vary along time. The size of the (usual) search population may also change in such a way that the sum of the individuals in the two populations does not exceed an established limit. The two populations have minimum and maximum sizes allowed that change according to the stage of the evolutionary process: if an alteration is detected in the environment, the search population increases its size in order to readapt quickly to the new conditions. When it is time to update memory, its size is increased if necessary. A genetic operator, inspired by the biological process of conjugation, is proposed and combined with this memory scheme. Our ideas were tested under different dynamics and compared with other approaches on two benchmark problems. The obtained results show the efficacy, efficiency and robustness of the investigated algorithm.