One Parameter Differential Evolution (OPDE) for Numerical Benchmark Problems
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Keywords:
Benchmark (surveying)
Differential Evolution
Simplicity
Among directions of intelligent computing, single objective optimization with real parameter is an important research focus. In recent, setting in single objective optimization with real parameter changes dramatically. Among types of population-based metaheuristic, differential evolution performs very outstandingly. So far, among the existing three crossover strategies, the binomial and exponential crossover is rarely used. However, IMODE, a DE algorithm suitable for the latter setting, is with the binomial and exponential crossover. In this paper, we optimize the parameter of the crossover strategy by experiment. According to our results, the original value of the parameter is not the best choice.
Differential Evolution
Binomial (polynomial)
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The performance of differential evolution (DE) algorithm largely depends on its crossover operator, whose substantive characteristics are to make the algorithm search in a subspace of the original search space. Different crossover operators use different subspace divisions, and how to choose a suitable crossover operator for a specific optimisation problem is still an open issue. This paper proposes variable-grouping-based exponential crossover (VGExp), where all variables are divided into multiple groups based on interaction information, and the variables that are mutated simultaneously have a high probability of coming from the same group. Moreover, the solutions can improve the accuracy of the variable grouping and provide initial guidance for optimisation. Therefore, the proposed VGExp seamlessly combines variables grouping technique and differential evolution. The experiment results based on 30 CEC2014 test problems show that VGExp can improve the performance of most DE variants, and it is also better than other well-developed crossover operators.
Differential Evolution
Operator (biology)
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Differential Evolution
Operator (biology)
Binomial (polynomial)
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In this paper, there are two mutation strategies and two crossover strategies are involved for enhancing solution searching ability of Differential Evolution (DE). These strategies will be activated according to current solution searching status. The elitist mutation will guide particles toward to solution space around the elitist particles, and the random to real-rand mutation can prevent particles form fall into local optimum. Both elitist crossover and one-cut-point crossover can produce potential particles for deeply search the basin of solution space. In the experiments, 25 test functions of CEC 2005 are adopted for testing performance of proposed method and compare it with 4 DE variants. From the results, it can be observed that the proposed method exhibits better than related works for solving most test functions.
Differential Evolution
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A blending crossover differential evolution algorithm is proposed to increase the precision of camera-space manipulation (CSM) system. In this approach, six view parameters and flattening parameter are assembled into a single parameter of blending crossover differential evolution; the positioning precision of camera-space manipulation is set to be a fitness function.The CSM system can obtain the optimal parameter combination by evolutionary iteration.Experimental results of a virtual robot system show the robot positioning precision is improved by blending crossover differential evolution algorithm.
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Flattening
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Differential Evolution
Perceptron
Backpropagation
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Dual Population Differential Evolution algorithm based on Crossover and Mutation strategy(CMDPDE) is proposed to enhance global search ability of single population differential evolution.In CMDPDE,one population uses big scale factor and crossover factor,the other with small scale factor and crossover factor will execute crossover or mutation operations to search better individual after an evolution for each individual evolves one time per generation.At the same time evolution information will be exchanged between two populations after all individuals of two populations evolve ten times.Compared with single population differential evolution,CMDPDE increases diversity of solutions through dual population and crossover and mutation strategy,which makes CMDPDE search better solutions in a larger range.Experiment results on six benchmark functions show that CMDPDE has the better ability of finding optimal solution.
Differential Evolution
Benchmark (surveying)
Scale factor (cosmology)
Factor (programming language)
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Differential Evolution
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In this paper, a Neural Networks optimizer based on Self-adaptive Differential Evolution is presented. This optimizer applies mutation and crossover operators in a new way, taking into account the structure of the network according to a per layer strategy. Moreover, a new crossover called interm is proposed, and a new self-adaptive version of DE called MAB-ShaDE is suggested to reduce the number of parameters. The framework has been tested on some well-known classification problems and a comparative study on the various combinations of self-adaptive methods, mutation, and crossover operators available in literature is performed. Experimental results show that DENN reaches good performances in terms of accuracy, better than or at least comparable with those obtained by backpropagation.
Differential Evolution
Backpropagation
Adaptive mutation
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