What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
Erik Cuevas spends much of his time researching Artificial intelligence, Algorithm, Evolutionary algorithm, Robustness and Mathematical optimization.
His Artificial intelligence research is multidisciplinary, incorporating elements of Multi-swarm optimization, Computer vision and Pattern recognition.
His research on Algorithm often connects related topics like Image segmentation.
His Evolutionary algorithm research includes themes of Premature convergence and Global optimization.
His research in Robustness focuses on subjects like Hough transform, which are connected to Range, Encoding and Local optimum.
His work on Optimization problem as part of general Mathematical optimization study is frequently linked to Process, bridging the gap between disciplines.
His most cited work include:
- A swarm optimization algorithm inspired in the behavior of the social-spider (272 citations)
- Parameter identification of solar cells using artificial bee colony optimization (196 citations)
- Kalman filter for vision tracking (123 citations)
What are the main themes of his work throughout his whole career to date?
Erik Cuevas mainly investigates Artificial intelligence, Algorithm, Optimization problem, Mathematical optimization and Robustness.
His studies deal with areas such as Computer vision and Pattern recognition as well as Artificial intelligence.
He combines subjects such as Image and Motion vector with his study of Algorithm.
His Optimization problem course of study focuses on Swarm behaviour and Swarm intelligence and Optimization algorithm.
His Robustness study combines topics in areas such as Digital image and Hough transform.
His Metaheuristic research is multidisciplinary, incorporating perspectives in Multi-swarm optimization and Global optimization.
He most often published in these fields:
- Artificial intelligence (42.37%)
- Algorithm (29.83%)
- Optimization problem (26.78%)
What were the highlights of his more recent work (between 2019-2021)?
- Metaheuristic (16.61%)
- Mathematical optimization (24.41%)
- Artificial intelligence (42.37%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Metaheuristic, Mathematical optimization, Artificial intelligence, Optimization problem and Algorithm.
His study in the field of Premature convergence and Metaheuristic algorithms is also linked to topics like Scheme and Maxima and minima.
Within one scientific family, Erik Cuevas focuses on topics pertaining to Pattern recognition under Artificial intelligence, and may sometimes address concerns connected to Image processing and Search algorithm.
His Optimization problem study integrates concerns from other disciplines, such as Evolutionary computation, Fuzzy logic, Swarm behaviour and Robustness.
Erik Cuevas interconnects Evolutionary algorithm and Cluster analysis in the investigation of issues within Robustness.
He has included themes like Optimization algorithm and Image in his Algorithm study.
Between 2019 and 2021, his most popular works were:
- An agent-based model to evaluate the COVID-19 transmission risks in facilities. (54 citations)
- From ants to whales: metaheuristics for all tastes (35 citations)
- A better balance in metaheuristic algorithms: Does it exist? (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Machine learning
Erik Cuevas mostly deals with Algorithm, Benchmark, Metaheuristic, Artificial intelligence and Mathematical optimization.
The Algorithm study combines topics in areas such as Evolutionary computation, Swarm behaviour, Noise and Computational intelligence.
His research in Benchmark intersects with topics in Small set, Robustness and Behavioral pattern.
His work investigates the relationship between Robustness and topics such as Chaotic that intersect with problems in Evolutionary algorithm.
His Artificial intelligence research integrates issues from Optimization algorithm and Pattern recognition.
His work on Optimization problem, IEEE Congress on Evolutionary Computation, Premature convergence and Continuous optimization as part of his general Mathematical optimization study is frequently connected to Maxima and minima, thereby bridging the divide between different branches of science.
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