Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Schemata evolution and building blocks
Evolutionary Computation
A dual-population genetic algorithm for adaptive diversity control
IEEE Transactions on Evolutionary Computation
An improved primal-dual genetic algorithm for optimization in dynamic environments
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
A new primal-dual genetic algorithm: case study for the winner determination problem
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Applied Soft Computing
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Genetic algorithms (GAs) are a class of search algorithms based on principles of natural evolution. Hence, incorporating mechanisms used in nature may improve the performance of GAs. In this paper inspired by the mechanisms of complementarity and dominance that broadly exist in nature, we present a new genetic algorithm -- Primal-Dual Genetic Algorithm (PDGA). PDGA operates on a pair of chromosomes that are primal-dual to each other through the primal-dual mapping, which maps one to the other with a maximum distance away in a given distance space in genotype. The primal-dual mapping improves the exploration capacity of PDGA and thus its searching efficiency in the search space. To test the performance of PDGA, experiments were carried out to compare PDGA over traditional simple GA (SGA) and a peer GA, called Dual Genetic Algorithm (DGA), over a typical set of test problems. The experimental results demonstrate that PDGA outperforms both SGA and DGA on the test set. The results show that PDGA is a good candidate genetic algorithm.