Eliminating drift bias from the differential evolution algorithm

Abstract

Differential evolution (DE) is an evolutionary algorithm designed for global numerical optimization. This chapter presents a new, rotationally invariant DE algorithm that eliminates drift bias from its trial vector generating function by projecting randomly chosen vector differences along lines of recombination. In this way, the natural distribution of vector differences drives both mutation and recombination. The new method also eliminates drift bias from survivor selection, leaving recombination as the only migration pathway. A suite of scalable test functions benchmarks the performance of drift-free DE against that of the algorithm from which it was derived. © 2008 Springer-Verlag Berlin Heidelberg.