Matches in DBpedia 2014 for { ?s ?p Dr. Enrique Alba is a professor of computer science at the University of Málaga, Spain. He got his Ph.D. degree on designing and analyzing parallel and distributed genetic algorithms. His current research interests involve the design and application of evolutionary algorithms, ant colony optimization, particle swarm optimization, and other bio-inspired systems to real problems including telecommunications, software engineering, combinatorial optimization, and bioinformatics among others. The main focus of all his work is on parallel metaheuristics and multiobjective optimization for complex problems.As to parallel metaheuristics, they are a new class of algorithms in that they are not just fast versions of sequential solvers, but they are usually running different numerical operations leading to different numerical solutions. This is why researchers should be careful in their development, performance assessment, and comparisons. These algorithms can combine numerical advances and physically parallel execution (GPUs, FPGAs, multicores, clusters, grids, and cloud) able of solving open problems and to take existing techniques to new scales of efficiency and accuraty.. }
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- Enrique_Alba abstract "Dr. Enrique Alba is a professor of computer science at the University of Málaga, Spain. He got his Ph.D. degree on designing and analyzing parallel and distributed genetic algorithms. His current research interests involve the design and application of evolutionary algorithms, ant colony optimization, particle swarm optimization, and other bio-inspired systems to real problems including telecommunications, software engineering, combinatorial optimization, and bioinformatics among others. The main focus of all his work is on parallel metaheuristics and multiobjective optimization for complex problems.As to parallel metaheuristics, they are a new class of algorithms in that they are not just fast versions of sequential solvers, but they are usually running different numerical operations leading to different numerical solutions. This is why researchers should be careful in their development, performance assessment, and comparisons. These algorithms can combine numerical advances and physically parallel execution (GPUs, FPGAs, multicores, clusters, grids, and cloud) able of solving open problems and to take existing techniques to new scales of efficiency and accuraty.".