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

Over the last few decades, many population-based swarm and evolutionary algorithms were
introduced in the literature. It is well known that population topology or sociometry plays an …

This paper introduces a new cellular genetic algorithm for solving multiobjective continuous
optimization problems. Our approach is characterized by using an external archive to store …

Research in exact algorithms, heuristics and metaheuristics for solving combinatorial
optimization problems is nowadays highly on the rise. The main advantage of using exact …

In this paper we study a number of issues related to the design of a cellular genetic
algorithm (cGA) for multiobjective optimization. We take as an starting point an algorithm …

In evolutionary computation, systematically structuring the population is used to manage the
evolution process. Thus controlling the amount of diversity during the algorithm search …

Vehicle traffic congestion is an increasing concern in metropolitan areas, with negative
implications for health, environment, and economy. Researchers, city managers, and …

Computational grids are an important emerging paradigm for large-scale distributed
computing. As grid systems become more wide-spread, techniques for efficiently exploiting …

When solving a symbolic regression problem, the gene expression programming (GEP)
algorithm could fall into a premature convergence which terminates the optimization process …

Over the past decade, subdividing evolutionary search into multiple local evolutionary
searches has been identified as an effective method to search for optimal solutions of multi …