Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through
their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the …

In the last 20 years, evolutionary algorithms (EAs) have shown to be an effective method to
solve multiobjective optimization problems (MOPs). Due to their population-based nature …

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 …

In evolutionary computation, the fitness function normally measures progress toward an
objective in the search space, effectively acting as an objective function. Through deception …

Reading Retail captures contemporary debates on the geography of retailing and
consumption spaces. It is constructed around a series of'readings' from key works, and is …

Coevolutionary algorithms behave in very complicated, often quite counterintuitive ways.
Researchers and practitioners have yet to understand why this might be the case, how to …

In this paper, we study the problem of constrained min-max optimization in a black-box
setting, where the desired optimizer cannot access the gradients of the objective function but …

Coevolutionary algorithms approach problems for which no function for evaluating potential
solutions is present or known. Instead, algorithms rely on the aggregation of outcomes from …

Novelty search is a recent artificial evolution technique that challenges traditional
evolutionary approaches. In novelty search, solutions are rewarded based on their novelty …

To reduce the computational complexity of training a deep neural network architecture using
large data sets of 3D scenes, a competitive learning scheme was devised. The proposed …