H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 66 Citations 16,658 407 World Ranking 1092 National Ranking 7

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Operating system
  • Statistics

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 most cited work include:

  • Parallelism and evolutionary algorithms (708 citations)
  • Parallel Metaheuristics: A New Class of Algorithms (410 citations)
  • SMPSO: A new PSO-based metaheuristic for multi-objective optimization (379 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Mathematical optimization (30.24%)
  • Metaheuristic (25.87%)
  • Evolutionary algorithm (22.04%)

What were the highlights of his more recent work (between 2015-2021)?

  • Evolutionary algorithm (22.04%)
  • Mathematical optimization (30.24%)
  • Metaheuristic (25.87%)

In recent papers he was focusing on the following fields of study:

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.

Between 2015 and 2021, his most popular works were:

  • Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments (110 citations)
  • Smart City and information technology: A review (52 citations)
  • Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities (40 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Operating system
  • Statistics

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Parallelism and evolutionary algorithms

E. Alba;M. Tomassini.
IEEE Transactions on Evolutionary Computation (2002)

1073 Citations

Parallel Metaheuristics: A New Class of Algorithms

Enrique Alba.
(2005)

828 Citations

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)

500 Citations

A survey of parallel distributed genetic algorithms

Enrique Alba;José M. Troya.
Complexity (1999)

487 Citations

The exploration/exploitation tradeoff in dynamic cellular genetic algorithms

E. Alba;B. Dorronsoro.
IEEE Transactions on Evolutionary Computation (2005)

447 Citations

Cellular genetic algorithms

Enrique Alba.
(2008)

445 Citations

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)

403 Citations

The jMetal framework for multi-objective optimization: Design and architecture

Juan J. Durillo;Antonio J. Nebro;Enrique Alba.
congress on evolutionary computation (2010)

345 Citations

AbYSS: Adapting Scatter Search to Multiobjective Optimization

Antonio J. Nebro;Francisco Luna;Enrique Alba;BernabÉ Dorronsoro.
IEEE Transactions on Evolutionary Computation (2008)

344 Citations

Software project management with GAs

Enrique Alba;J. Francisco Chicano.
Information Sciences (2007)

321 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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