ParadisEO: a framework for parallel and distributed biologically inspired heuristics

Abstract

In this paper we present ParadisEO, an open source framework for flexible parallel and distributed design of hybrid metaheuristics. Flexibility means that the parameters such as data representation and variation operators can be evolved. It is inherited from the EO object-oriented library for evolutionary computation. ParadisEO provides different parallel and/or distributed models and allows a transparent multi-threaded implementation. Moreover, it supplies different natural hybridization mechanisms mainly for metaheuristics including evolutionary algorithms and local search methods. The framework is experimented here in the spectroscopic data mining field. The flexibility property allowed an easy and straightforward development of a geneticalgorithm-based attribute selection for models discovery in NIR spectroscopic data. Experiments on a cluster of SMPs (IBM SP3) show that a good speed-up is achieved by using the provided parallel distributed models and multi-threading. Furthermore, the hybridization of the GA with the efficient PLS method allows to discover high-quality models. Indeed, their accuracy and understandability are improved respectively by 37% and 88%.