Title :
Selection strategies and random perturbations in differential evolution
Author_Institution :
Fac. of Electr. Eng., Univ. of Ljubljana, Ljubljana, Slovenia
Abstract :
Differential evolution is a simple algorithm for global optimization. Basically it consists of three operations: mutation, crossover and selection. Despite many research papers dealing with the first two, hardly any attention has been paid to the third one nor is there a place for this operation in the algorithm basic naming scheme. In the paper we show that employing different selection strategies combined with some random perturbation of population vectors notably improves performance in high-dimensional problems. Further analysis of results shows that the improvement is statistically significant.
Keywords :
optimisation; search problems; crossover operation; differential evolution; direct search methods; global optimization; high-dimensional problems; mutation operation; performance improvement; population vectors; random perturbations; selection operation; selection strategies; Algorithm design and analysis; Convergence; Evolution (biology); Noise measurement; Optimization methods; Vectors; differential evolution; direct search methods; global optimization; heuristic;
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
DOI :
10.1109/CEC.2012.6256446