Scalability of optimization algorithms is a major challenge in co** with the ever-growing
size of optimization problems in a wide range of application areas from high-dimensional …

This article is the second part of a two-part survey series on large-scale global optimization.
The first part covered two major algorithmic approaches to large-scale optimization, namely …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong
in 1994 and since then many CCEAs have been proposed and successfully applied to …

Fitness landscapes were proposed in 1932 as an abstract notion for understanding
biological evolution and were later used to explain evolutionary algorithm behaviour. The …

Cooperative co-evolution (CC) is an evolutionary computation framework that can be used
to solve high-dimensional optimization problems via a “divide-and-conquer” mechanism …

In this paper we use a divide-and-conquer approach to tackle large-scale optimization
problems with overlap** components. Decomposition for an overlap** problem is …

Large-scale global optimization (LSGO) problems have widely appeared in various real-
world applications. However, their inherent complexity, coupled with the curse of …

The divide-and-conquer strategy has been widely used in cooperative co-evolutionary
algorithms to deal with large-scale global optimization problems, where a target problem is …

Problem decomposition plays an essential role in the success of cooperative co-evolution
(CC), when used for solving large-scale optimization problems. The recently proposed …