What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Artificial intelligence
His primary areas of investigation include Multi-objective optimization, Mathematical optimization, Metaheuristic, Benchmark and Optimization problem.
He combines subjects such as Genetic algorithm and Asynchronous communication with his study of Multi-objective optimization.
His Mathematical optimization study incorporates themes from Convergence and Crossover.
His studies in Metaheuristic integrate themes in fields like Java, Algorithm design and Approximation algorithm.
His Benchmark research is under the purview of Artificial intelligence.
His work in the fields of Probabilistic-based design optimization, Test functions for optimization and Engineering optimization overlaps with other areas such as Engineering design process and Topology optimization.
His most cited work include:
- jMetal: A Java framework for multi-objective optimization (785 citations)
- SMPSO: A new PSO-based metaheuristic for multi-objective optimization (379 citations)
- The jMetal framework for multi-objective optimization: Design and architecture (246 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Multi-objective optimization, Metaheuristic, Mathematical optimization, Optimization problem and Evolutionary algorithm.
His Multi-objective optimization research includes themes of Particle swarm optimization, Distributed computing, Pareto principle and Benchmark.
The Metaheuristic study combines topics in areas such as Java, Set and Domain.
His Mathematical optimization research incorporates elements of Algorithm and Convergence.
His Optimization problem research includes elements of Python, Time complexity and Big data.
His Evolutionary algorithm study combines topics in areas such as Computational intelligence, Theoretical computer science, Frequency assignment and GSM.
He most often published in these fields:
- Multi-objective optimization (58.54%)
- Metaheuristic (47.15%)
- Mathematical optimization (43.90%)
What were the highlights of his more recent work (between 2017-2021)?
- Multi-objective optimization (58.54%)
- Metaheuristic (47.15%)
- Optimization problem (33.33%)
In recent papers he was focusing on the following fields of study:
Antonio J. Nebro focuses on Multi-objective optimization, Metaheuristic, Optimization problem, Mathematical optimization and Evolutionary algorithm.
His Multi-objective optimization research incorporates themes from Cellular evolutionary algorithm, Distributed computing and Artificial intelligence, Benchmark.
The study incorporates disciplines such as Software and Operations research in addition to Metaheuristic.
His studies in Optimization problem integrate themes in fields like Python, Transparency, Ambiguity and Big data.
The concepts of his Mathematical optimization study are interwoven with issues in Merge sort and Sorting algorithm.
His Evolutionary algorithm research focuses on Set and how it connects with Consistency, Fuzzy logic and State.
Between 2017 and 2021, his most popular works were:
- jMetalPy: A Python framework for multi-objective optimization with metaheuristics (34 citations)
- A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME (32 citations)
- InDM2: Interactive Dynamic Multi-Objective Decision Making Using Evolutionary Algorithms (13 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Artificial intelligence
The scientist’s investigation covers issues in Optimization problem, Mathematical optimization, Evolutionary algorithm, Preference and Multi-objective optimization.
Antonio J. Nebro interconnects Evolutionary computation, Genetic algorithm, Machine learning and Swarm intelligence in the investigation of issues within Optimization problem.
Antonio J. Nebro studies Mathematical optimization, namely Pareto principle.
His Evolutionary algorithm study incorporates themes from State, Operator, Selection and Sorting algorithm.
His research in Multi-objective optimization focuses on subjects like Python, which are connected to Visualization.
As a part of the same scientific family, Antonio J. Nebro mostly works in the field of Region of interest, focusing on Big data and, on occasion, Conflicting objectives, Analytics and Semantic data model.
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