The notion of a population with individuals which are replaced by newly generated
individuals is a pervasive idea in differential evolution (DE). Techniques such as archives …

One of the issues that might affect the performance of Differential Evolution (DE) is
premature convergence. In such cases, and especially in long-term executions, due to the …

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 …

Differential evolution (DE) is one of the most influential optimization algorithms up-to-date.
DE works through analogous computational steps as used by a standard evolutionary …

The performance of Differential Evolution (DE) is closely related to the population diversity
since its mechanism of generating offspring depends wholly on the differences between …

For the differential evolution (DE) algorithms, there are many parameter adaptation methods,
which aim at tuning the mutation factor F and the crossover probability CR. When the …

This chapter gives an overview of Differential Evolution (DE), then presents adaptive and
self-adaptive mechanisms within the DE algorithm. They can be used in order to make a DE …

Differential evolution (DE) is one of the most popular paradigms of evolutionary algorithms.
Numerous variants of basic DE have been developed in the past two decades after it was …

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 …

In differential evolution (DE), the salient feature lies in its mutationmechanismthat
distinguishes it from other evolutionary algorithms. Generally, for most of the DE algorithms …