The automatic design of parameter adaptation techniques for differential evolution with genetic programming

2022 ◽  
pp. 108070
Author(s):  
Vladimir Stanovov ◽  
Shakhnaz Akhmedova ◽  
Eugene Semenkin
Keyword(s):  
Genetic Programming ◽  
Differential Evolution ◽  
Automatic Design ◽  
Parameter Adaptation

scholarly journals Differential Evolution with Population and Strategy Parameter Adaptation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
V. Gonuguntla ◽  
R. Mallipeddi ◽  
Kalyana C. Veluvolu
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Optimization Problems ◽  
Search Space ◽  
Benchmark Problems ◽  
Parameter Adaptation ◽  
De Algorithm ◽  
Probabilistic Technique ◽  
Number Of Individuals ◽  
Strategy Parameters

Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, which can automatically update the parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the works do not control the adaptation of the population size. In addition, they try to adapt each strategy parameters individually but do not take into account the interaction between the parameters that are being adapted. In this paper, we introduce a DE algorithm where both strategy parameters are self-adapted taking into account the parameter dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. In addition, the proposed DE algorithm starts by sampling a huge number of sample solutions in the search space and in each generation a constant number of individuals from huge sample set are adaptively selected to form the population that evolves. The proposed algorithm is evaluated on 14 benchmark problems of CEC 2005 with different dimensionality.

Distance based parameter adaptation for Success-History based Differential Evolution

2019 ◽  
Vol 50 ◽  
pp. 100462 ◽  
Author(s):  
Adam Viktorin ◽  
Roman Senkerik ◽  
Michal Pluhacek ◽  
Tomas Kadavy ◽  
Ales Zamuda
Keyword(s):  
Differential Evolution ◽  
Parameter Adaptation

scholarly journals AUTOMATIC DESIGN OF NONCRYPTOGRAPHIC HASH FUNCTIONS USING GENETIC PROGRAMMING

2014 ◽  
Vol 30 (4) ◽  
pp. 798-831 ◽  
Author(s):  
César Estébanez ◽  
Yago Saez ◽  
Gustavo Recio ◽  
Pedro Isasi
Keyword(s):  
Genetic Programming ◽  
Hash Functions ◽  
Automatic Design
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