Feature selection using multi-objective evolutionary algorithms : application to cardiac SPECT diagnosis

An optimization methodology based on the use of Multi-Objective Evolutionary Algorithms (MOEA) in order to deal with problems of feature selection in data mining was proposed. For that purpose a Support Vector Machines (SVM) classifier was adopted. The aim being to select the best features and optimize the classifier parameters simultaneously while minimizing the number of features necessary and maximize the accuracy of the classifier and/or minimize the errors obtained. The validity of the methodology proposed was tested in a problem of cardiac Single Proton Emission Computed Tomography (SPECT). The results obtained allow one to conclude that MOEA is an efficient feature selection approach and the best results were obtained when the accuracy, the errors and the classifiers parameters are optimized simultaneously

Feature selection for bankruptcy prediction: a multi-objective optimization approach

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the classifier while keeping the number of features low. A two-objective problem - minimization of the number of features and accuracy maximization – was fully analyzed using two classifiers, Logistic Regression (LR) and Support Vector Machines (SVM). Simultaneously, the parameters required by both classifiers were also optimized. The validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The method proposed can provide useful information for the decision maker in characterizing the financial health of a company

Multi-objective ant colony optimization for the twin-screw configuration problem

The Twin-Screw Configuration Problem (TSCP) consists in identifying the best location of a set of available screw elements along a screw shaft. Due to its combinatorial nature, it can be seen as a sequencing problem. In addition, different conflicting objectives may have to be considered when defining a screw configuration and, thus, it is usually tackled as a multi-objective optimization problem. In this research, a multi-objective ant colony optimization (MOACO) algorithm was adapted to deal with the TSCP. The influence of different parameters of the MOACO algorithm was studied and its performance was compared with that of a previously proposed multi-objective evolutionary algorithm and a two-phase local search algorithm. The experimental results showed that MOACO algorithms have a significant potential for solving the TSCP.This work has been supported by the Portuguese Fundacao para a Ciencia e Tecnologia under PhD grant SFRH/BD/21921/2005. Thomas Stutzle acknowledges support of the Belgian F.R.S-FNRS of which he is a research associate, the E-SWARM project, funded by an ERC Advanced Grant, and by the Meta-X project, funded by the Scientific Research Directorate of the French Community of Belgium

Dominance, indicator and decomposition based search for multi-objective QAP: landscape analysis and automated algorithm selection

International audienceWe investigate the properties of large-scale multi-objective quadratic assignment problems (mQAP) and how they impact the performance of multi-objective evolutionary algorithms. The landscape of a diversified dataset of bi-, multi-, and many-objective mQAP instances is characterized by means of previously-identified features. These features measure complementary facets of problem difficulty based on a sample of solutions collected along random and adaptive walks over the landscape. The strengths and weaknesses of a dominance-based, an indicator-based, and a decomposition-based search algorithm are then highlighted by relating their expected approximation quality in view of landscape features. We also discriminate between algorithms by revealing the most suitable one for subsets of instances. At last, we investigate the performance of a feature-based automated algorithm selection approach. By relying on low- cost features, we show that our recommendation system performs best in more than of the considered mQAP instances

Calibración de un algoritmo de optimización multiobjetivo en problemas de secuenciación de operaciones

En este trabajo de final de máster se profundiza en la parametrización y análisis de los resultados obtenidos con un algoritmo de colonia de hormigas resolviendo problemas complejos de secuenciación de operaciones multicriterio. En primer lugar se implementa la opción de resolver las secuencias en modo permutation. Luego se toma la decisión de qué métricas utilizar para realizar una correcta valoración de los resultados y se desarrolla una herramienta para poder obtener el valor de esas métricas de forma automática. Por último se lleva a cabo la experimentación, que consiste en una búsqueda de la configuración idónea de los parámetros para la obtención de la mejor optimización multicriterio, y una análisis comparativo entre la resolución en modo permutation y modo non-permutation

Hybrid population-based algorithms for the bi-objective quadratic assignment problem

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Hybrid population-based algorithms for the bi-objective quadratic assignment problem

Published in Journal of Mathematical Modelling and AlgorithmsAIDA-04-11info:eu-repo/semantics/publishe

Cooperation in self-organized heterogeneous swarms

Cooperation in self-organized heterogeneous swarms is a phenomenon from nature with many applications in autonomous robots. I specifically analyzed the problem of auto-regulated team formation in multi-agent systems and several strategies to learn socially how to make multi-objective decisions. To this end I proposed new multi-objective ranking relations and analyzed their properties theoretically and within multi-objective metaheuristics. The results showed that simple decision mechanism suffice to build effective teams of heterogeneous agents and that diversity in groups is not a problem but can increase the efficiency of multi-agent systems

Designing screws for polymer compounding in twin-screw extruders

Tese de doutoramento em Ciência e Engenharia de Polímeros e CompósitosConsidering its modular construction, co-rotating twin screw extruders can be easily adapted to work with polymeric systems with more stringent specifications. However, their geometrical flexibility makes the performance of these machines strongly dependent on the screw configuration. Therefore, the definition of the adequate screw geometry to use in a specific polymer system is an important process requirement which is currently achieved empirically or using a trial-and-error basis. The aim of this work is to develop an automatic optimization methodology able to define the best screw geometry/configuration to use in a specific compounding/reactive extrusion operation, reducing both cost and time. This constitutes an optimization problem where a set of different screw elements are to be sequentially positioned along the screw in order to maximize the extruder performance. For that, a global modeling program considering the most important physical, thermal and rheological phenomena developing along the axis of an intermeshing co-rotating twin screw extruder was initially developed. The accuracy and sensitivity of the software to changes in the input parameters was tested for different operating conditions and screw configurations using a laboratorial Leistritz LSM 30.34 extruder. Then, this modeling software was integrated into an optimization methodology in order to be possible solving the Twin Screw Configuration Problem. Multi-objective versions of local search algorithms (Two Phase Local Search and Pareto Local Search) and Ant Colony Optimization algorithms were implemented and adapted to deal with the combinatorial, discrete and multi-objective nature of the problem. Their performance was studied making use of the hypervolume indicator and Empirical Attainment Function, and compared with the Reduced Pareto Search Genetic Algorithm (RPSGA) previously developed and applied to this problem. In order to improve the quality of the results and/or to decrease the computational cost required by the optimization methodology, different hybrid algorithms were tested. The approaches developed considers the use of local search procedures (TPLS and PLS algorithms) into population based metaheuristics, as MOACO and MOEA algorithms. Finally, the optimization methodology developed was applied to the optimization of a starch cationization reaction. Several starch cationization case studies, involving different screw elements screw lengths and conflicting objectives, were tested in order to validate this technique and to prove the potential of this automatic optimization methodology.Devido à sua construção modular, as extrusoras de duplo-fuso co-rotativas podem ser facilmente adaptadas a sistemas poliméricos que requerem especificações mais rigorosas. No entanto, esta flexibilidade geométrica torna o seu desempenho fortemente dependente da configuração do parafuso. Por isso, a tarefa de definir a melhor configuração do parafuso para usar num determinado sistema polimérico é um requisito importante do processo que é actualmente realizada empiricamente ou utilizando um processo de tentativa erro. O objectivo principal deste trabalho é desenvolver uma metodologia automática de optimização que seja capaz de definir a melhor configuração/geometria do parafuso a usar num determinado sistema de extrusão, reduzindo custos e tempo. Este problema é um problema de optimização, onde os vários elementos do parafuso têm que ser sequencialmente posicionados ao longo do eixo do parafuso de forma a maximizar o desempenho da extrusora. Para isso, foi inicialmente desenvolvido um programa de modelação que considera os mais importantes fenómenos físicos, térmicos e reológicos que ocorrem ao longo da extrusora de duplo fuso co-rotativa. De forma a testar a precisão e a sensibilidade do software às alterações dos parâmetros, diversas condições operativas e configurações de parafuso foram testadas tendo como base uma extrusora laboratorial Leistritz LSM 30.34. Seguidamente, este software de modelação foi integrado numa metodologia de optimização com vista à resolução do problema de configuração da extrusora de duplo-fuso. Para lidar com a natureza combinatorial, discreta e multi-objectiva do problema em estudo, foram adaptadas e implementadas versões multi-objectivas de algoritmos de procura local (Two-Phase Local Search and Pareto Local Search) e Ant Colony Optimization. O desempenho dos diversos algoritmos foi estudado usando o hipervolume e as Empirical Attainment Functions. Os resultados foram comparados com os resultados obtidos com o algoritmo genético Reduced Pareto Search Genetic Algorithm (RPSGA) desenvolvido e aplicado anteriormente a este problema. Com o objectivo de melhorar a qualidade dos resultados e/ou diminuir o esforço computacional exigido pela metodologia de optimização, foram testadas diversas hibridizações. Os algoritmos híbridos desenvolvidos consideram a integração de algoritmos de procura local (TPLS e PLS) noutras metheuristicas, como MOACO e MOEA. Por fim, a metodologia de optimização desenvolvida neste trabalho foi testada na optimização de uma reacção de cationização do amido. Para validar esta técnica e provar o seu potencial, foram realizados vários estudos envolvendo diferentes elementos e comprimentos de parafusos, bem como, a optimização de objectivos em conflito