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Differential Evolution (DE) is a simple yet efficient stochastic algorithm for solving real world problems. Binomial crossover and exponential crossover are two commonly used crossover operators in current popular DE. It is noteworthy that these two operators can only generate a vertex of a hypercube defined by the mutant and target vectors. In this paper, using intermediate recombination to generate...
Elites have been widely used in many evolutionary algorithms. However, only elites in current generation are utilized to guide the learning/updating of particles/individuals in existing algorithms. Usually, elites in different generations are different and elites in the past generations may contain experienced knowledge and thus may be helpful for guiding particles/individuals to promising areas....
Large scale optimization has become an important and challenging area in evolutionary computation. To solve this kind of problems efficiently, this paper proposes a multiple parents guided differential evolution (MPGDE) algorithm. Instead of using only one parent to guide each individual in traditional DE variants, multiple top ranked parents are utilized to direct each individual to search the space...
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