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Parallel and distributed evolutionary algorithms

Parallel and distributed evolutionary algorithms

Prof. El-ghazali Talbi
Professor Computer Science
University of Lille (France)


Abstract:

Parallel and distributed computing can be used in the design and implementation of metaheuristics (e.g. evolutionary algorithms) for speedup the search, improve the quality of the obtained solutions, improve the robustness of the obtained solutions, and solve large scale problems.

From the algorithmic design point of view, we will present the main parallel models for metaheuristics (algorithmic level, iteration level, solution level). We will address also:

                  - Parallel hybrid models with exact methods.
                  - Parallel models for multi-objective optimization.
                  - Illustrations solving large challenging applications in telecommunications, logistics
                    and transportation and bioinformatics.

From the implementation point of view, we here concentrate on the parallelization of metaheuristics on general-purpose parallel and distributed architectures, since this is the most widespread computational platform. The rapid evolution of technology in terms of processors (GPUs, multi-core), networks (Infiniband), and architectures (Clouds, clusters) make those architectures very popular nowadays.

Different architectural criteria which affect the efficiency of the implementation will be considered: shared memory / distributed memory, homogeneous / heterogeneous, dedicated / non dedicated, local network / large network. Indeed, those criteria have a strong impact on the deployment technique employed such as load balancing and fault-tolerance. Finally, some software frameworks for parallel metaheuristics such as PARADISEO are presented. Those frameworks allow the design of parallel and hybrid metaheuristics for mono-objective and multi-objective optimization, and the transparent implementation on different parallel and distributed architectures using adapted middleware.

Biography:

Prof. El-ghazali Talbi received the Master and Ph.D degrees in Computer Science, both from the Institut National Polytechnique de Grenoble in France. Then he became an Associate Professor in Computer Sciences at the University of Lille (France). Since 2001, he is a full Professor at the University of Lille and the head of the optimization group of the Computer Science laboratory (LIFL). His current research interests are in the field of multi-objective optimization, parallel algorithms, metaheuristics, combinatorial optimization, cluster and grid computing, hybrid and cooperative optimization, and application to logistics/transportation, bioinformatics and networking.

Professor Talbi has to his credit more than 300 publications in journals, chapters in books, and conferences. He is the co-editor of five books. He was a guest editor of more than 10 special issues in different journals (Journal of Heuristics, Journal of Parallel and Distributed Computing, European Journal of Operational Research, Theoretical Computer Science, Journal of Global Optimization). He is the head of the INRIA Dolphin project and the bioinformatics platform of the Genopole of Lille. He has many collaborative national, European and international projects.

He is the co-founder and the coordinator of the research group dedicated to Metaheuristics: Theory and Applications (META). He is the founding co-chair of the NIDISC workshop on nature inspired computing (IEEE/ACM IPDPS). He served in different capacities on the programs of more than 100 national and international conferences. He is also the organizer of many conferences (e.g. EA'2005, ROADEF'2006, META'2008, IEEE AICCSA'2010).

 

The length of the tutorial:
two hours.

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