Abstract. This paper presents the evolving objects library (EOlib), an object-oriented framework for evolutionary computation (EC) that aims to provide a flexible set of classes to build EC applications. EOlib design objective is to be able to evolve any object in which fitness makes sense. In order to do so, EO concentrates on interfaces; any object can evolve if it is endowed with an interface to do so. In this paper, we describe what features an object must have in order to evolve, and some examples of how EO has been put to practice evolving neural networks, solutions to the Mastermind game, and other novel applications.
Abstract. This paper describes the recently released DREAM (Distributed Resource Evolutionary Algorithm Machine) framework for the automatic distribution of evolutionary algorithm (EA) processing through a virtual machine built from large numbers of individual machines linked by standard Internet protocols. The framework allows five different user entry points which depend on the knowledge and requirements of the user. At the highest level, users may specify and run distributed EAs simply by manipulating graphical displays. At the lowest level the framework turns becomes a P2P (Peer to Peer) mobile agent system, that may be used for the automatic distribution of a class of processes including, but not limited to, EAs.
Abstract. In the last few years, the coevolutionary paradigm has shown an increasing interest thanks to its high ability to manage huge search spaces. Particularly, the cooperative interaction scheme is recommendable when the problem solution may be decomposable in subcomponents and there are strong interdependencies among them. The paper introduces a novel application of these algorithms to the learning of fuzzy rule-based systems for system modeling. Traditionally, this process is performed by sequentially designing their different components. However, we propose to accomplish a simultaneous learning process with cooperative coevolution to properly consider the tight relation among the components, thus obtaining more accurate models.
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