2023 - Research.com Computer Science in Spain Leader Award
2022 - Research.com Computer Science in Spain Leader Award
Enrique Alba mainly investigates Mathematical optimization, Metaheuristic, Genetic algorithm, Multi-objective optimization and Evolutionary algorithm. His Mathematical optimization research includes themes of Algorithm, Vehicle routing problem, Set and Benchmark. His Metaheuristic research is multidisciplinary, relying on both Memetic algorithm, Particle swarm optimization, Ant colony optimization algorithms and Implementation.
His work deals with themes such as Parallel algorithm, Partitioned global address space, Asynchronous communication and Artificial intelligence, which intersect with Genetic algorithm. Enrique Alba has included themes like Convergence, Differential evolution, Evolutionary computation, Optimization problem and Crossover in his Multi-objective optimization study. His Evolutionary algorithm research incorporates themes from Heuristics and Cellular automaton.
His primary areas of study are Mathematical optimization, Metaheuristic, Evolutionary algorithm, Genetic algorithm and Artificial intelligence. The study incorporates disciplines such as Set and Benchmark in addition to Mathematical optimization. His biological study spans a wide range of topics, including Simulated annealing, Particle swarm optimization and Ant colony optimization algorithms.
His Evolutionary algorithm research is multidisciplinary, incorporating perspectives in Evolutionary computation, Software, Theoretical computer science and Distributed computing. He combines subjects such as Parallel algorithm, Algorithm, Parallel computing, Asynchronous communication and Distributed algorithm with his study of Genetic algorithm. His research ties Machine learning and Artificial intelligence together.
His scientific interests lie mostly in Evolutionary algorithm, Mathematical optimization, Metaheuristic, Artificial intelligence and Optimization problem. His Evolutionary algorithm research integrates issues from Scheme, Scalability, Distributed computing and Transport engineering. His Mathematical optimization research focuses on Set and how it connects with Anytime algorithm, Field and Multi-objective optimization.
His Metaheuristic research includes elements of Genetic algorithm, Particle swarm optimization, Energy and Parallel algorithm. His work in Genetic algorithm covers topics such as Simulated annealing which are related to areas like Variable neighborhood search. His Artificial intelligence study frequently links to other fields, such as Machine learning.
His primary areas of investigation include Evolutionary algorithm, Mathematical optimization, Artificial intelligence, Machine learning and Transport engineering. The various areas that Enrique Alba examines in his Evolutionary algorithm study include Distributed computing, Maximization, Metric, Pareto principle and Software. His work on Premature convergence, Metaheuristic and Optimization problem as part of general Mathematical optimization research is often related to Duration, thus linking different fields of science.
Enrique Alba interconnects Representation, Global optimization and Remanufacturing in the investigation of issues within Metaheuristic. His research in the fields of Recurrent neural network, Neuroevolution and Artificial neural network overlaps with other disciplines such as Random error. Enrique Alba has researched Machine learning in several fields, including Sampling and Occupancy.
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Parallelism and evolutionary algorithms
E. Alba;M. Tomassini.
IEEE Transactions on Evolutionary Computation (2002)
Parallel Metaheuristics: A New Class of Algorithms
SMPSO: A new PSO-based metaheuristic for multi-objective optimization
A.J. Nebro;J.J. Durillo;J. Garcia-Nieto;C.A. Coello Coello.
multiple criteria decision making (2009)
A survey of parallel distributed genetic algorithms
Enrique Alba;José M. Troya.
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
E. Alba;B. Dorronsoro.
IEEE Transactions on Evolutionary Computation (2005)
Cellular genetic algorithms
MOCell: A cellular genetic algorithm for multiobjective optimization
Antonio J. Nebro;Juan J. Durillo;Francisco Luna;Bernabé Dorronsoro.
nature inspired cooperative strategies for optimization (2009)
AbYSS: Adapting Scatter Search to Multiobjective Optimization
Antonio J. Nebro;Francisco Luna;Enrique Alba;BernabÉ Dorronsoro.
IEEE Transactions on Evolutionary Computation (2008)
Software project management with GAs
Enrique Alba;J. Francisco Chicano.
Information Sciences (2007)
The jMetal framework for multi-objective optimization: Design and architecture
Juan J. Durillo;Antonio J. Nebro;Enrique Alba.
congress on evolutionary computation (2010)
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