Métaheuristiques pour la résolution de problème de covoiturage régulier de grande taille et d'une extension

La dispersion spatiale de l'habitat et des activités de ces dernières décennies a fortement contribué à un allongement des distances et des temps de trajets domicile-travail. Cela a pour conséquence un accroissement de l'utilisation des voitures particulières, notamment au sein et aux abords des grandes agglomérations. Afin de réduire les impacts dus à l'augmentation du trafic routier, des services de covoiturage, où des usagers ayant la même destination se regroupent en équipage pour se déplacer, ont été mis en place partout dans le monde. Nous présentons ici nos travaux sur le problème de covoiturage régulier. Dans cette thèse, le problème de covoiturage régulier a été modélisé et plusieurs métaheuristiques de résolution ont été implémentées, testées et comparées. La thèse est organisée de la façon suivante: tout d'abord, nous commençons par présenter la définition et la description du problème ainsi que le modèle mathématique associé. Ensuite, plusieurs métaheuristiques pour résoudre le problème sont présentées. Ces approches sont au nombre de quatre: un algorithme de recherche locale à voisinage variable, un algorithme à base de colonies de fourmis, un algorithme génétique guidée et un système multi-agents génétiques auto-adaptatif. Des expériences ont été menées pour démontrer l'efficacité de nos approches. Nous continuons ensuite avec la présentation et la résolution d'une extension du problème de covoiturage occasionel comportant plusieurs destinations. Pour terminer, une plate-forme de test et d'analyse pour évaluer nos approches et une plate-forme de covoiturage sont présentées dans l'annexe.Nowadays, the increased human mobility combined with high use of private cars increases the load on environment and raises issues about quality of life. The extensive use of private cars lends to high levels of air pollution, parking problem, traffic congestion and low transfer velocity. In order to ease these shortcomings, the car pooling program, where sets of car owners having the same travel destination share their vehicles, has emerged all around the world. We present here our research on the long-term car pooling problem. In this thesis, the long-term car pooling problem is modeled and metaheuristics for solving the problem are investigated. The thesis is organized as follows. First, the definition and description of the problem as well as its mathematical model are introduced. Then, several metaheuristics to effectively and efficiently solve the problem are presented. These approaches include a Variable Neighborhood Search Algorithm, a Clustering Ant Colony Algorithm, a Guided Genetic Algorithm and a Multi-agent Self-adaptive Genetic Algorithm. Experiments have been conducted to demonstrate the effectiveness of these approaches on solving the long-term car pooling problem. Afterwards, we extend our research to a multi-destination daily car pooling problem, which is introduced in detail manner along with its resolution method. At last, an algorithm test and analysis platform for evaluating the algorithms and a car pooling platform are presented in the appendix.ARRAS-Bib.electronique (620419901) / SudocSudocFranceF

Semantic Segmentation Network Stacking with Genetic Programming

Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2023). Semantic Segmentation Network Stacking with Genetic Programming. Genetic Programming And Evolvable Machines, 24(2 — Special Issue on Highlights of Genetic Programming 2022 Events), 1-37. [15]. https://doi.org/10.1007/s10710-023-09464-0---Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), AICE (DSAIPA/DS/0113/2019), UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, and by the grant SFRH/BD/137277/2018.Semantic segmentation consists of classifying each pixel of an image and constitutes an essential step towards scene recognition and understanding. Deep convolutional encoder–decoder neural networks now constitute state-of-the-art methods in the field of semantic segmentation. The problem of street scenes’ segmentation for automotive applications constitutes an important application field of such networks and introduces a set of imperative exigencies. Since the models need to be executed on self-driving vehicles to make fast decisions in response to a constantly changing environment, they are not only expected to operate reliably but also to process the input images rapidly. In this paper, we explore genetic programming (GP) as a meta-model that combines four different efficiency-oriented networks for the analysis of urban scenes. Notably, we present and examine two approaches. In the first approach, we represent solutions as GP trees that combine networks’ outputs such that each output class’s prediction is obtained through the same meta-model. In the second approach, we propose representing solutions as lists of GP trees, each designed to provide a unique meta-model for a given target class. The main objective is to develop efficient and accurate combination models that could be easily interpreted, therefore allowing gathering some hints on how to improve the existing networks. The experiments performed on the Cityscapes dataset of urban scene images with semantic pixel-wise annotations confirm the effectiveness of the proposed approach. Specifically, our best-performing models improve systems’ generalization ability by approximately 5% compared to traditional ensembles, 30% for the less performing state-of-the-art CNN and show competitive results with respect to state-of-the-art ensembles. Additionally, they are small in size, allow interpretability, and use fewer features due to GP’s automatic feature selection.publishersversionepub_ahead_of_prin

Reinforcement learning based local search for grouping problems: A case study on graph coloring

Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult. In this paper, we propose a general solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with descent-based local search. The viability of the proposed approach is verified on a well-known representative grouping problem (graph coloring) where a very simple descent-based coloring algorithm is applied. Experimental studies on popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves competitive performances compared to a number of well-known coloring algorithms

Evolutionary design of deep neural networks

Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of the topology of artificial neural networks, with most works focusing on very simple architectures. However, times have changed, and nowadays convolutional neural networks are the industry and academia standard for solving a variety of problems, many of which remained unsolved before the discovery of this kind of networks. Convolutional neural networks involve complex topologies, and the manual design of these topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to use neuroevolution in order to evolve the architecture of convolutional neural networks. To do so, we have decided to try two different techniques: genetic algorithms and grammatical evolution. We have implemented a niching scheme for preserving the genetic diversity, in order to ease the construction of ensembles of neural networks. These techniques have been validated against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%, and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275. Both results have proven very competitive when compared with the state of the art. Also, in all cases, ensembles have proven to perform better than individual models. Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced in 2017, which includes more samples and a set of letters for character recognition. Results have shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures can be reused across domains with similar characteristics. In summary, neuroevolution is an effective approach for automatically designing topologies for convolutional neural networks. However, it still remains as an unexplored field due to hardware limitations. Current advances, however, should constitute the fuel that empowers the emergence of this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917. This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Francisco Javier Segovia Pérez.- Vocal: Simon Luca

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications