Parameter Setting with Dynamic Island Models

In this paper we proposed the use of a dynamic island model which aim at adapting parameter settings dynamically. Since each island corresponds to a specific parameter setting, measuring the evolution of islands populations sheds light on the optimal parameter settings efficiency throughout the search. This model can be viewed as an alternative adaptive operator selection technique for classic steady state genetic algorithms. Empirical studies provide competitive results with respect to other methods like automatic tuning tools. Moreover, this model could ease the parallelization of evolutionary algorithms and can be used in a synchronous or asynchronous way

Modèles en iles pour le réglage automatique de paramètres : application au problème du bandit manchot

National audienc

Pourquoi rendre les modèles en iles autonomes ?

Date du colloque : 04/2012National audienc

Non stationary operator selection with island models

The purpose of adaptive operator selection is to choose dynamically the most suitable variation operator of an evolutionary algorithm at each iteration of the search process. These variation operators are applied on individuals of a population which evolves, according to an evolutionary process, in order to find an optimal solution. Of course the efficiency of an operator may change during the search and therefore its application should be precisely controlled. In this paper, we use dynamic island models as operator selection mechanisms. A sub-population is associated to each operators and individuals are allowed to migrate from one sub-population to another one. In order to evaluate the performance of this adaptive selection mechanism, we propose an abstract operator representation using fitness improvement distributions that allow us to define non stationary operators with mutual interactions. Our purpose is to show that the adaptive selection is able to identify not only good operators but also suitable sequences of operators

SAT Encoding and CSP Reduction for Interconnected Alldiff Constraints

Constraint satisfaction problems (CSP) or Boolean satisfiability problem (SAT) are two well known paradigm to model and solve combinatorial problems. Modeling and resolution of CSP is often strengthened by global constraints (e.g., Alldiff constraint). This paper highlights two different ways of handling specific structural information: a uniform propagation framework to handle (interleaved) Alldiff constraints with some CSP reduction rules; and a SAT encoding of these rules that preserves the reduction properties of CSP

Strategies for handling uncertainty in tactical distribution planning - an empirical study

International audienc

A Metaheuristic for Tactical Inventory Planning in Closed-Loop Supply Chains

International audienc

Model and Combinatorial Optimization Methods for Tactical Planning in Closed-Loop Supply Chains

International audienc

Tactical Supply Chain Distribution Planning In The Telecommunications Service Industry

Supply chains are ubiquitous across industries and a considerable e ort has been invested in supply chain management techniques over the last two decades. In equipment-intensive service industries, it often involves repair operations. In this context, tactical inventory planning is concerned with optimally planning supplies and repairs based on demand forecasts and in face of con icting business objectives. It is based on a case study in the telecommunications sector where large quantities and varieties of spare parts are required for service maintenance and repair tasks at customer premises or company exchanges. Speci cally, we consider a multi-echelon spare parts supply chain and tackle the problem of determining an optimal stock distribution plan given a demand forecast. We propose a mixed integer programming and a metaheuristic approach to this problem. The model is open to a variety of network topologies, site functions and transfer policies. It also accommodates multiple objectives by the means of a weighted cost function. We report experiments on pseudo-random instances designed to evaluate plan quality and impact of cost weightings. In particular, we show how appropriate weightings allow to emulate common planning strategies (e.g., just-in-time replenishment, minimal repair). We also assess plan quality and system performance against di erent classes of pseudo-random instances featuring different volume and distribution of stock and demand