adaptive evolutionary algorithms
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2017 ◽  
Vol 11 (1) ◽  
pp. 1-12 ◽  
Author(s):  
José Alberto Bonilla-Vera ◽  
Jaime Mora-Vargas ◽  
Miguel González-Mendoza ◽  
Iván Adrian López-Sánchez ◽  
César Jaime Montiel-Moctezuma

2013 ◽  
Vol 303-306 ◽  
pp. 1494-1500
Author(s):  
Jian Wei Wang ◽  
Jian Ming Zhang

Aiming at effectively overcoming the disadvantages of traditional evolutionary algorithm which converge slowly and easily run into local extremism, an improved adaptive evolutionary algorithms is proposed. Firstly, in order to choose the optimal objective fitness value from the population in every generation, the absolute and relative fitness are defined. Secondly, fuzzy technique is adopted to adjust the weights of objective functions, crossover probability, mutation probability, crossover positions and mutation positions during the iterative process. Finally, three classical test functions are given to illustrate the validity of improved adaptive evolutionary algorithm, simulation results show that the diversity and practicability of the optimal solution set are better by using the proposed method than other multi-objective optimization methods.


Author(s):  
Konstantinos Theofilatos ◽  
Andreas Karathanasopoulos ◽  
Georgios Sermpinis ◽  
Thomas Amorgianiotis ◽  
Efstratios Georgopoulos ◽  
...  

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