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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the(More)
Research inmetaheuristics for combinatorial optimizationproblemshas lately experienced anoteworthy shift towards the hybridization of metaheuristics with other techniques for optimization. At the same time, the focus of research has changed from being rather algorithm-oriented to being more problemoriented. Nowadays the focus is on solving the problem at(More)
In this work, we introduce a multiagent architecture called the MultiAGent Metaheuristic Architecture (MAGMA) conceived as a conceptual and practical framework for metaheuristic algorithms. Metaheuristics can be seen as the result of the interaction among different kinds of agents: The basic architecture contains three levels, each hosting one or more(More)
Portfolio selection is a relevant problem arising in finance and economics. While its basic formulations can be efficiently solved through linear or quadratic programming, its more practical and realistic variants, which include various kinds of constraints and objectives, have in many cases to be tackled by approximate algorithms. In this work, we present(More)
This paper describes the behavior observed in a class of cellular automata that we have defined as “dissipative”, i.e., cellular automata for which the external environment can somehow inject “energy” to dynamically influence the evolution of the automata. In this class of cellular automata, we have observed that stable macro-level global structures emerge(More)