E.: ParadisEO-MOEO: A framework for evolutionary multi-objective optimization. In: Evolutionary multicriterion optimization

Abstract. This paper presents ParadisEO-MOEO, a white-box object-oriented generic framework dedicated to the flexible design of evolu-tionary multi-objective algorithms. This paradigm-free software embeds some features and techniques for Pareto-based resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear concep-tual distinction between the solution methods and the multi-objective problems they are intended to solve. This separation confers a maxi-mum design and code reuse. ParadisEO-MOEO provides a broad range of archive-related features (such as elitism or performance metrics) and the most common Pareto-based fitness assignment strategies (MOGA, NSGA, SPEA, IBEA and more). Furthermore, parallel and distributed models as well as hybridization mechanisms can be applied to an al-gorithm designed within ParadisEO-MOEO using the whole version of ParadisEO. In addition, GUIMOO, a platform-independant free software dedicated to results analysis for multi-objective problems, is briefly in-troduced. Key words: object-oriented frameworks, design and code reuse, multi-objective optimization, evolutionary algorithms