Soft Computing

, Volume 21, Issue 8, pp 2105–2127

Differential Evolution with Grid-Based Parameter Adaptation

  • Vasileios A. Tatsis
  • Konstantinos E. Parsopoulos
Methodologies and Application

DOI: 10.1007/s00500-015-1911-2

Cite this article as:
Tatsis, V.A. & Parsopoulos, K.E. Soft Comput (2017) 21: 2105. doi:10.1007/s00500-015-1911-2


The reduction of human intervention in tuning metaheuristic optimization algorithms has been an ongoing research pursuit. Differential Evolution is a very popular algorithm that counts a large number of variants. However, its efficiency has been shown to depend on the type of its crossover operators (binomial or exponential), mutation operators, as well as on the two parameters that dominate these procedures. Making proper decisions on these parameters has proved to be a laborious, problem-dependent task. We propose a parameter adaptation technique that allows the algorithm to dynamically determine the most suitable crossover type and parameter values during its execution. The technique is based on a search procedure in the discretized parameter search space, using estimations of the algorithm’s performance. The proposed approach is tested and statistically validated on an established high-dimensional test suite. Also, comparisons with other algorithms are reported, verifying the competitiveness of the proposed approach.


Metaheuristic Optimization Dynamic Parameter Adaptation  Parameter Control Differential Evolution 

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Vasileios A. Tatsis
    • 1
  • Konstantinos E. Parsopoulos
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of IoanninaIoanninaGreece

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