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 …

Identification of variable interaction is essential for an efficient implementation of a divide-
and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an …

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 …

Cooperative co-evolution (CC) is an explicit means of problem decomposition in
multipopulation evolutionary algorithms for solving large-scale optimization problems. For …

Through introducing the divide-and-conquer strategy, cooperative co-evolution (CC) has
been successfully employed by many evolutionary algorithms (EAs) to solve large-scale …

Cooperative co-evolutionary algorithms have addressed many large-scale problems
successfully, but the nonseparable large-scale problems with overlapping subcomponents …

Cooperative Co-evolution (CC) is a promising framework for solving large-scale optimization
problems. However, the round-robin strategy of CC is not an efficient way of allocating the …

Although cooperative coevolutionary algorithms are developed for large-scale dynamic
optimization via subspace decomposition, they still face difficulties in reacting to …

Cooperative co-evolution (CC) is a practical and efficient evolutionary framework for solving
large-scale global optimization problems (LSGOPs). The performance of CC depends on …

Cooperative co-evolution (CC) is an explicit means of divide-and-conquer strategy in
evolutionary algorithms for solving large-scale optimization problems. The subcomponents …