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.
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