Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces

TLDR

The authors propose a new heuristic for minimizing nonlinear, non‑differentiable continuous functions. Using a De Jong testbed, the method is shown to converge faster and more reliably than Adaptive Simulated Annealing and Annealed Nelder–Mead. It requires few control variables, is robust, user‑friendly, and well suited to parallel computation. Contact: [e-mail]; [e-mail]; 1947 Center Street, Berkeley, CA.

Abstract

A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. __________________________________________ 1)International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1198, Suite 600, Fax: [phone]. E-mail: [e-mail]. On leave from Siemens AG, ZFE T SN 2, OttoHahn-Ring 6, D-81739 Muenchen, Germany. Fax: [phone], Email:[e-mail]. 2)836 Owl Circle, Vacaville, CA 95687, [e-mail].