Abstract: Hybrid evolutionary metaheuristics tend to enhance search capabilities, by improving intensication and diversication, through incorporating dierent cooperative metaheuristics. In this paper, an improved version of the Hybrid Evolutionary Metaheuristics (HEMH) [7] is presented. Unlike HEMH, HEMH2 uses simple inverse greedy algorithm to construct its initial population. Then, the search eorts is directed to improve these solutions by exploring the search space using binary differential evolution. After a certain number of evaluations, path-relinking is applied on high quality solutions to investigate the non-visited regions in the search space. During evaluations, the dynamic-sized neighborhood structure is adopted to shrink/extend the mating/updating range. Furthermore, the Pareto adaptive epsilon concept is used to control the archiving process with preserving the extreme solutions. HEMH2 is verified against its predecessor HEMH and the MOEA/D [13], using a set of MOKSP instances from the literature. The experimental results indicate that the HEMH2 is highly competitive and can achieve better results.

Abstract: Hybrid Metaheuristics aim to incorporate and combine different metaheuristics with each other to enhance the search capabilities. It can improve both of intensification and diversification toward the preferred solutions and concentrates the search efforts to investigate the promising regions in the search space. In this paper, a comparative study was developed to study the effect of the hybridization of different metaheuristics within MOEA/D framework. We study four proposals of hybridization, the first proposal is to combine adaptive discrete differential evolution operator with MOEA/D. The second one is to combine the path-Relinking operator with MOEA/D. the third and the fourth proposals combine both of them in MOEA/D. The comparative study uses a set of MOKSP instances commonly used in the literature to investigate the hybridization effects as well as a set of quality assessment indicators. The experimental results indicate that the proposals are highly competitive for most test instances and can be considered as viable alternatives.

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Ref HAL: hal-00624096_v1
Abstract: Handling Multiobjective Optimization Problems (MOOP) using Hybrid Metaheuristics represents a promising and interest area of research. In this paper, a Hybrid Evolutionary Metaheuristics (HEMH) is presented. It combines different metaheuristics integrated with each other to enhance the search capabilities. It improves both of intensification and diversification toward the preferred solutions and concentrates the search efforts to investigate the promising regions in the search space. In the proposed HEMH, the search process is divided into two phases. In the first one, the DM-GRASP is applied to obtain an initial set of high quality solutions dispersed along the Pareto front. Then, the search efforts are intensified on the promising regions around these solutions through the second phase. The greedy randomized path-relinking with local search or reproduction operators are applied to improve the quality and to guide the search to explore the non discovered regions in the search space. The two phases are combined with a suitable evolutionary framework supporting the integration and cooperation. Moreover, the efficient solutions explored over the search are collected in an external archive. The HEMH is verified and tested against some of the state of the art MOEAs using a set of MOKSP instances commonly used in the literature. The experimental results indicate that the HEMH is highly competitive and can be considered as a viable alternative.

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Ref HAL: hal-00624080_v1
DOI: 10.1007/s11517-011-0785-4
Abstract: In the PREVENIR-5 study, artificial neural networks (NN) were applied to a large sample of patients with recent first acute coronary syndrome (ACS) to identify determinants of persistence of evidence-based cardiovascular medications (EBCM: antithrombotic + beta-blocker + statin + angiotensin converting enzyme inhibitor-ACEI and/or angiotensin-II receptor blocker-ARB). From October 2006 to April 2007, 1811 general practitioners recruited 4850 patients with a mean time of ACS occurrence of 24 months. Patient profile for EBCM persistence was determined using automatic rule generation from NN. The prediction accuracy of NN was compared with that of logistic regression (LR) using Area Under Receiver-Operating Characteristics-AUROC. At hospital discharge, EBCM was prescribed to 2132 patients (44%). EBCM persistence rate, 24 months after ACS, was 86.7%. EBCM persistence profile combined overweight, hypercholesterolemia, no coronary artery bypass grafting and low educational level (Positive Predictive Value=0.958). AUROC curves showed better predictive accuracy for NN compared to LR models.

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Ref HAL: hal-00605389_v1
Abstract: BELMANDT is the collective pseudonym under which a group of mathematicians and computer scientists have decide to publish their common works on Pretopology and to give a nod to BOURBAKI. These works would never have been realized without the dynamic leadership of professor Marcel Brissaud, now retired, and who is of course associated to the publication of this book. Marcel Brissaud, during the seventies, has been the first to observe the interest of developing mathematical concepts adapted to humanities. Pretopology theory relies on the statement that the axiomatic of mathematical topology is too strong to model the phenomena of social sciences. This axiomatic has to be reconsidered to give birth to a theory that is adapted to data issued from these fields of expertise, better than trying to force data to fit to an existing and inadequate theory. This book is the first English revised version of a first book, published in French, which is planned to be the first of a series dedicated to the whole set of results obtained in Pretopology: continuity, connectedness, associated metric... Many applications of Pretopology were conducted in various fields: economy with the structural analysis of a economy, game theory with the study of the dynamics of coalitions in cooperative games, sociology with analysis of social networks, data analysis with the construction of clustering algorithms, pattern recognition with digital image analysis as in mathematical morphology, etc. Purely mathematical work in Pretopology has enabled the building of processing algorithms that have been put in a software library, PRETOPOLIB, allowing researchers to implement a pretopological approach in their work. We wish the reader of this book the pleasure of exploring the basics of Pretopology and we hope that he will find the materials for his own work and we give him a rendezvous for the next books on the subject.