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Plenary Lecture
Annealing Hybrid Algorithms Strategies for
NP Class Problems
Professor Juan Frausto-Solis
Tecnologico de Monterrey, Campus Cuernavaca
Autopista del Sol km 104,
Colonia Real del Puente,
62790, Xochitepec, Morelos
MEXICO
Email: juan.frausto@itesm.mx
Web Page:
http://campus.cva.itesm.mx/jfrausto/Curriculum/publications.htm
Abstract: One of the main objectives of Computer Science is to
develop algorithms for helping human beings to solve their difficult and
important problems with speed and quality. Among the more difficult
computational problems are those belonging to the NP Hard class; examples of
these problems are: Satisfiability Problem (SAT), Scheduling (including the
allocation of tasks in operating systems and robotics), planning and most
problems related with bioinformatics such as phylogenetic trees
construction, Folding problems and many others. There are many stochastic
approaches proposed for these problems but none of them is always the best
solution. A very good approach is to use Simulated Annealing hybridised
(i.e. mixed) with other approaches. Among these approaches we can find the
very formal ones as Mechanical Statistical, Markov Models, semi-formal as
Support Vector Machines and Neural Networks, and very informal as Golden
Ratio. In this presentation the main hybridization approaches, applications
and challenges for future research are presented.
Brief Biography of the Speaker:
Ph.D. in Electrical Engineering, Institut National Polytechnique de Grenoble
& Ecole Central de Lyon (France). He is full professor at Technologico de
Monterrey Campus Cuernavaca where he is the leader of Combinatorial
Optimization Research Group. He has published many papers related with
hybrid optimization methods such as Simulated Annealing, Genetic algorithms,
Tabu Search, Support Vector Machines, and Linear Programming with
applications to Scheduling, Bioinformatics, Satisfiability, Data Mining and
many others. His main interest is to develop better algorithms for NP-Hard
problems.
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