Abstract Population size of Differential Evolution (DE) algorithms is often specified by user
and remains fixed during run. During the first decade since the introduction of DE the …

A new mechanism for the adaptation of the population size in differential evolution (DE) is
described. The adaptive mechanism is based on linear reduction of the population diversity …

Structural bias is a recently identified property of optimisation algorithms, causing them to
favour certain regions of the search space over others, independently of the objective …

Differential evolution (DE) is one of the most popular paradigms of evolutionary algorithms.
In general, DE does not exploit distribution information provided by the population and, as a …

In this paper, we investigate the effect of population size on the quality of solutions and the
computational effort required by the Differential evolution (DE) Algorithm. A set of 5 problems …

Over the last few decades, a number of differential evolution (DE) algorithms have been
proposed with excellent performance on mathematical benchmarks. However, like any other …

A key parameter affecting the operation of differential evolution (DE) is the crossover rate Cr
ϵ [0, 1]. While very low values are recommended for and used with separable problems, on …

An effective and efficient successful-parent-selecting framework is proposed to improve the
performance of differential evolution (DE) by providing an alternative for the selection of …

Differential evolution (DE) algorithms compose an efficient type of evolutionary algorithm
(EA) for the global optimization domain. Although it is well known that the population …

Differential Evolution (DE) is one of the most effective approaches in Evolutionary
Computation to tackling complex black-box optimization, however, the overall performance …