The increasing complexity of real-world optimization problems raises new challenges to
evolutionary computation. Responding to these challenges, distributed evolutionary …

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 …

In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that
explore the space of potential solutions by building and sampling explicit probabilistic …

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

Estimation of distribution algorithms (EDAs) are a set of algorithms that belong to the field of
Evolutionary Computation. Characterized by the use of probabilistic models to represent the …

Since estimation of distribution algorithms (EDAs) were proposed, many attempts have been
made to improve EDAs' performance in the context of global optimization. So far, the studies …

Estimation of Distribution Algorithms (EDAs) belong to the class of population based
optimisation algorithms. They are motivated by the idea of discovering and exploiting the …

Abstract Estimation of Distribution Algorithms (EDAs) require flexible probability models that
can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are …

Finding near-optimal solutions in an acceptable amount of time is a challenge when
developing sophisticated approximate approaches. A powerful answer to this challenge …