Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Immunological Computation: Theory and Applications
Immunological Computation: Theory and Applications
ARO: A new model-free optimization algorithm inspired from asexual reproduction
Applied Soft Computing
Swarm Intelligence: Introduction and Applications
Swarm Intelligence: Introduction and Applications
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Improving clonal colony optimization to evolve robust solutions
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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In the last half century, computer science has witnessed the appearance of nature-inspired methods for the resolution of complex optimization problems, which are hardly solved by traditional optimization methods. Metaheuristics like evolutionary algorithms or swarm intelligence have been successfully applied to a wide range of both theoretical and practical problems. This article presents a new optimization method based in reproduction mechanics of plant clonal colonies. These systems are composed of a set of clones, interconnected and spatially spread over a geographical area. In this new metaheuristic, called Clonal Colony Optimization (CCO), problem solutions are associated to clones, that are subject to evolutionary cycles that adaptively reconfigure the geographical covering over the search space of the problem. Solutions coded in this manner would be more robust that those obtained using independent individuals.