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Abstract:This article proposes a methodology to design Serious Escape Games (SEG) for teaching. It describes, through the proposed model, how to approach the various aspects related to this type of activity (constraints, pedagogy, parameterization, tests, background, etc.). The goal of SEG is not only to reinforce the knowledge and skills of students or to acquire them, but also to improve their intuition as to the choice of the skills to be mobilized according to the different situations. This methodology was implemented through an experiment carried out as part of a Masters combinatorial optimization course in an Engineering School, the aim of which was to increase the attractiveness of this very complex discipline manipulating a large number of digits. The evaluation of this experiment showed the advantages of this activity : manipulation of the notions in a playful way by the learners, increase of the motivation and improvement of their reactivity and practical sense.

Abstract:This document presents a general-purpose software framework dedicated to the design, the analysis and the implementation of local search algorithms: ParadisEO-MO. A substantial number of single-solution based local search metaheuristics has been proposed so far, and an attempt of unifying existing approaches is here presented. Based on a fine-grained decomposition, a conceptual model is proposed and is validated by regarding a number of state-of-the-art methodologies as simple variants of the same structure. This model is then incorporated into the ParadisEO-MO software framework. This framework has proven its efficiency and high flexibility by enabling the resolution of many academic and real-world optimization problems from science and industry.

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This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective 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 conceptual distinction between the solution methods and the problems they are intended to solve. This separation confers a maximum design and code reuse. This general-purpose framework provides a broad range of fitness assignment strategies, the most common diversity preservation mechanisms, some elitistrelated features as well as statistical tools. Furthermore, a number of state-of-the-art search methods, including NSGA-II, SPEA2 and IBEA, have been implemented in a user-friendly way, based on the fine-grained ParadisEO-MOEO components