Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem

We consider the university course timetabling problem, which is one of the most studied problems in educational timetabling. In particular, we focus our attention on the formulation known as the curriculum-based course timetabling problem, which has been tackled by many researchers and for which there are many available benchmarks. The contribution of this paper is twofold. First, we propose an effective and robust single-stage simulated annealing method for solving the problem. Secondly, we design and apply an extensive and statistically-principled methodology for the parameter tuning procedure. The outcome of this analysis is a methodology for modeling the relationship between search method parameters and instance features that allows us to set the parameters for unseen instances on the basis of a simple inspection of the instance itself. Using this methodology, our algorithm, despite its apparent simplicity, has been able to achieve high quality results on a set of popular benchmarks. A final contribution of the paper is a novel set of real-world instances, which could be used as a benchmark for future comparison

L'écart salarial entre les secteurs public et privé au Québec

Rapport de recherche présenté à la Faculté des arts et des sciences en vue de l'obtention du grade de Maîtrise en sciences économiques

Single- and multi-objective genetic programming: new bounds for weighted order and majority

We consolidate the existing computational complexity analysis of genetic programming (GP) by bringing together sound theoretical proofs and empirical analysis. In particular, we address computational complexity issues arising when coupling algorithms using variable length representation, such as GP itself, with different bloat-control techniques. In order to accomplish this, we first introduce several novel upper bounds for two single- and multi-objective GP algorithms on the generalised Weighted ORDER and MAJORITY problems. To obtain these, we employ well-established computational complexity analysis techniques such as fitness-based partitions, and for the first time, additive and multiplicative drift. The bounds we identify depend on two measures, the maximum tree size and the maximum population size, that arise during the optimization run and that have a key relevance in determining the runtime of the studied GP algorithms. In order to understand the impact of these measures on a typical run, we study their magnitude experimentally, and we discuss the obtained findings.Anh Nguyen, Tommaso Urli, Markus Wagnerhttp://www.sigevo.org/foga-2013

Accurately Measuring the Satisfaction of Visual Properties in Virtual Camera Control

International audienceAbstract. Declarative approaches to camera control model inputs as properties on the camera and then rely on constraint-based and/or optimization techniques to compute the camera parameters or paths that best satisfy those properties. To reach acceptable performances, such approaches often (if not always) compute properties satisfaction in an approximate way. Therefore, it is difficult to measure results in terms of accuracy, and also compare approaches that use different approxima- tions. In this paper, we propose a simple language which can be used to express most of the properties proposed in the literature and whose semantics provide a way to accurately measure their satisfaction. The language can be used for several purposes, for example to measure how accurate a specific approach is and to compare two distinct approaches in terms of accuracy

Current situation facing the needs of the scenarios from the deliverables I2.1.1 and I2.2.1

In this document we present the main issues that we have to face in order to define a Software Product Line (SPL) for Broadcasting Systems. These issues were identified through requirement analysis and refactoring of SEDUITE which are described in two internal deliverables: a) D.2.2.1: Introduces the requirements (functional and non-functional) of a Broadcasting System by using a case study based on large gatherings (e.g., concerts, competitions, parties, etc.). b) D.2.1.1: Explains the definition of SEDUITE as a SPL by identifying the different assets and products that make part of it. In particular, from each deliverable different questions were raised. We use these questions to identify the issues that we need to face and to guide the redaction of this document. We classify the questions according to three main topics: (i) user assistance (cf. Section 2), (ii) building and evolution of the SPL (cf. Section 3) and (iii) kinds of variability (cf. Section 4) The questions from the D.2.2.1 deliverable are identified with I.x and those from D.2.1.1 with Q.x. In both cases, the 'x' represents the number of the question in the deliverable. Additionally, we include the results of two questionnaires intended for consumers of information (i.e., professor and students) from broadcasting system in academic institutions

SPLEMMA: A Generic Framework for Controlled-Evolution of Software Product Lines

International audienceManaging in a generic way the evolution process of feature- oriented Software Product Lines (SPLs) is complex due to the number of elements that are impacted and the heterogeneity of the SPLs regarding artifacts used to define them. Existing work presents specific approaches to manage the evolution of SPLs in terms of such artifacts, i.e., assets, feature models and relation definitions. Moreover stakeholders do not necessarily master all the knowledge of the SPL making its evolution difficult and error-prone without a proper tool support. In order to deal with these issues, we introduce SPLEmma, a generic framework that follows a Model Driven Engineering approach to capture the evolution of a SPL independently of the kind of assets, technologies or feature models used for the product derivation. Authorized changes are described by the SPL maintainer and captured in a model used to generate tools that guide the evolution process and preserve the consistency of the whole SPL. We report on the application of our approach on two SPLs: YourCast for digital signage systems, and SALOON, which enables generation of configurations for cloud providers

Hybrid meta-heuristics for combinatorial optimization

Combinatorial optimization problems arise, in many forms, in vari- ous aspects of everyday life. Nowadays, a lot of services are driven by optimization algorithms, enabling us to make the best use of the available resources while guaranteeing a level of service. Ex- amples of such services are public transportation, goods delivery, university time-tabling, and patient scheduling. Thanks also to the open data movement, a lot of usage data about public and private services is accessible today, sometimes in aggregate form, to everyone. Examples of such data are traffic information (Google), bike sharing systems usage (CitiBike NYC), location services, etc. The availability of all this body of data allows us to better understand how people interacts with these services. However, in order for this information to be useful, it is necessary to develop tools to extract knowledge from it and to drive better decisions. In this context, optimization is a powerful tool, which can be used to improve the way the available resources are used, avoid squandering, and improve the sustainability of services. The fields of meta-heuristics, artificial intelligence, and oper- ations research, have been tackling many of these problems for years, without much interaction. However, in the last few years, such communities have started looking at each other’s advance- ments, in order to develop optimization techniques that are faster, more robust, and easier to maintain. This effort gave birth to the fertile field of hybrid meta-heuristics.openDottorato di ricerca in Ingegneria industriale e dell'informazioneopenUrli, Tommas