SIALAC Benchmark: On the design of adaptive algorithms for traffic lights problems

International audienceOptimizing traffic lights in road intersections is a mandatory step to achieve sustainable mobility and efficient public transportation in modern cities. Several mono or multi-objective optimization methods exist to find the best traffic signals settings, such as evolutionary algorithms, fuzzy logic algorithms, or even particle swarm optimizations. However, they are generally dedicated to very specific traffic configurations. In this paper, we introduce the SIALAC benchmark bringing together about 24 real-world based study cases, and investigate fitness landscapes structure of these problem instances

Combinatorial Surrogate-Assisted Optimization for Bus Stops Spacing Problem

International audienceThe distribution of transit stations constitutes an ubiquitous task in large urban areas. In particular, bus stops spacing is a crucial factor that directly affects transit ridership travel time. Hence, planners often rely on traffic surveys and virtual simulations of urban journeys to design sustainable public transport routes. However, the combinator-ial structure of the search space in addition to the time-consuming and black-box traffic simulations require computationally expensive efforts. This imposes serious constraints on the number of potential configurations to be explored. Recently, powerful techniques from discrete optimization and machine learning showed convincing to overcome these limitations. In this preliminary work, we build combinatorial surrogate models to approximate the costly traffic simulations. These so-trained surrog-ates are embedded in an optimization framework. More specifically, this article is the first to make use of a fresh surrogate-assisted optimization algorithm based on the mathematical foundations of discrete Walsh functions in order to solve the real-world bus stops spacing optimization problem. We conduct our experiments with the sialac benchmark in the city of Calais, France. We compare state-of-the-art approaches and we highlight the accuracy and the optimization efficiency of the proposed methods

High performance genetic programming on GPU

The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when pro-grammed in the CUDA language. We compare two par-allelization schemes that evaluate several GP programs in parallel. We show that the fine grain distribution of compu-tations over the elementary processors greatly impacts per-formances. We also present memory and representation op-timizations that further enhance computation speed, up to 2.8 billion GP operations per second. The code has been developed with the well known ECJ library

Algorithmes bio-mimétiques pour la reconnaissance de formes et l'apprentissage

Dans cette thèse, nous appliquons deux algorithmes bio-mimétiques à la résolution d un problème de biologie marine : la détection de structures rétentives en eaux côtières. Nous confrontons ces deux méthodes, à savoir un algorithme à colonies de fourmis et la programmation génétique, avec des méthodes dites classiques (analyse physique, streamlines ) et mettons en évidence les difficultés de ces dernières à traiter ce problème, rendu difficile par la proximité des côtes induisant de fortes perturbations de courant. Pour pallier ce problème, nous proposons tout d abord une adaptation de l algorithme à colonies de fourmis tel que défini par Marco Dorigo, introduisant les notions de biais, de multiples colonies et d évaporation instantanée de la phéromone. Cette méthode se révèle performante et ses détections sont d une qualité satisfaisant les exigences des biologistes. Nous proposons ensuite une adaptation de la programmation génétique inspirée des travaux de Jason Daida sur la détection de crêtes de pressions sur la croûte glaciaire. Nous introduisons le concept de génération de filtres itératifs, technique permettant la prise en compte et la propagation d informations globales. Cette méthode se révèle, elle aussi performante, mais n est pas directement utilisable car elle ne permet pas d identifier les enveloppes des structures rétentives. Elle met plutôt en évidence des zones rétentives.In this thesis, we apply two bio-mimetic algorithms to solve a marine biology problem : retentive structures detection in coastal waters. We compare these two methods (an ant based algorithm and genetic programming) to classical methods (physical analysis, streamlines...) and we draw out their difficulties to solve this problem, made difficult by the proximity of the coast inducing strong stream perturbations. To solve this problem, we present an adaptation of the ant based algorithm defined by Marco Dorigo, introducing the notion of bias, several colonies and instantaneous pheromone evaporation. This method appears to be efficient and its detections are good enough to satisfy biologists. We also propose an adaptation of genetic programming inspired of the work of Jason Daida about detection of peaks of pressure. We introduce the concept of oterative filters generation which allows to take into account and propagate global informations. This method appears to be also very efficient, but is not directly usable because it is not able to detect the envelops of retentive structures, but it rather draws out retentive zones.CALAIS-BU Sciences (621932101) / SudocSudocFranceF

Towards Human-Competitive Game Playing for Complex Board Games with Genetic Programming

International audienceRecent works have shown that Genetic Programming (GP) can be quite successful at evolving human-competitive strategies for games ranging from classic board games, such as chess, to action video games. However to our knowledge GP was never applied to modern complex board games, so-called eurogames, such as Settlers of Catan, i.e. board games that typically involve four characteristics: they are non zero-sum games, multiplayer, with hidden information and random elements. In this work we study how GP can evolve artificial players from low level attributes of a eurogame named " 7 Wonders " , that features all the characteristics of this category. We show that GP can evolve competitive artificial intelligence (AI) players against human-designed AI or against Monte Carlo Tree Search, a standard in automatic game playing

ANTabu

this paper, we show how we transpose this mechanism to a parallel hybrid parallel ants method called ANTabu to solve the well known QAP and how we achieve very good results by improving the Gambardella, Taillard and Dorigo scheme [10]. This paper is organized as follows: Section 2 deals with the ant system applied to the QAP. Section 3 introduces ANTabu. Our experimental results are presented and compared with other schemes in Sect. 4. We draw some preliminaries conclusions in Sect. 5. 2 Ant system for QA

Développement d'un paradigme d'optimisation par Hommilière et application à l'enseignement assisté par ordinateur sur Internet

Cette thèse décrit la mise en place d'un Intelligent Tutoring System (ITS) dans le logiciel de soutien scolaire de la société Paraschool pour aider les élèves à naviguer sur le site. Le système est maintenant opérationnel et a été testé en ligne sur maintenant 250 000 utilisateurs accédant au site depuis Internet. L'optimisation par Hommilière développée dans cette thèse découle d'un premier essai d'utilisation d'un algorithme d'Optimisation par Colonie de Fourmis (OCF) qui a montré ses limites. Contrairement aux fourmis artificielles, les utilisateurs ne sont pas contrôlables : il ne faut pas compter sur un altruisme excessif de leur part, ils ont une activité discontinue (vacances), ils nécessitent un parcours pédagogique personnalisé,...bref, les modifications devant être apportées à l'OCF étaient telles qu'il a fallu se rendre à l'évidence que l'utilisation collective d'élèves humains à des fins d'optimisation était un paradigme différent que nous avons appelé "Optimisation par Hommilière". Une deuxième contribution de cette thèse est la mise en place d'un système de notation automatique permettant d'évaluer automatiquement élèves et exercices, fondé sur la notation ELO utilisées dans le monde des échecs. Outre un puissant outil de suggestion d'exercices, le système a montré qu'il est aussi un très puissant outil de vérification du bon fonctionnement du logiciel d'e-learning, capable de détecter dans les exercices non seulement les erreurs syntaxiques, sémantiques mais aussi pédagogiques. Pour finir, tous les sites Web fréquentés en temps réel par de nombreux utilisateurs peuvent bénéficier de cette technique pour optimiser leur contenu, leur structure.This thesis describes the implementation of an Intelligent Tutoring System (ITS) within the existing e-learning software of the Paraschool company, in order to help students to find their way among thousands of different items. The system is operationnal, and the different versions have been tested for real on more than 250 000 users that use the site over the Internet. The man-hill optimization technique described in this document stems from a first attempt to use an Ant Colony Optimisation (ACO) algorithm, which revealed unsuited for the task. To the opposite of artificial ants, human students are not controllable : it is not possible to count on innate altruism, their activity is variable (holidays), each students needs a specific treatment,...All in all, the modifications that needed to be applied to the ACO paradigm were so numerous that it became obvious that the collective use of human students for optimization was indeed a different paradigm that we called "man-hill optimization". This thesis also contains a contribution to the automatic rating of students and items (exercises) based on the ELO chess rating system. Beyond being a powerful tool for suggesting good exercices, the system showed that it is also very powerful to make sure the e-learning software works well, as it is capable of finding exercices that contain not only syntaxic, but also semantic errors. the system can also point out exercices that are not well placed in the pedagogic progression. Finally, all web sites browsed by many users can benefit from this technique to optimize their contents, their structure and make sure that all is going well.CALAIS-BU Sciences (621932101) / SudocSudocFranceF