2010 | OriginalPaper | Chapter
Adaptive Differential Evolution with p-Best Crossover for Continuous Global Optimization
Authors : Sk Minhazul Islam, Saurav Ghosh, Subhrajit Roy, Swagatam Das
Published in: Swarm, Evolutionary, and Memetic Computing
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Differential Evolution (DE) is arguably one of the most powerful stochastic real parameter optimization algorithms in current use. DE operates through the similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike the traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used, which makes the scheme self-organizing in this respect. Its performance, however, is still quite dependent on the setting of control parameters such as the mutation factor and the crossover probability according to both experimental studies and theoretical analyses. Our aim is to design a DE algorithm with control parameters such as the scale factor and the crossover constants adapting themselves to different problem landscapes avoiding any user intervention. Further to improve the convergence performance an innovative crossover mechanism is proposed here.