What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Artificial intelligence
- Algorithm
Carlos M. Fonseca spends much of his time researching Mathematical optimization, Multi-objective optimization, Evolutionary algorithm, Genetic algorithm and Constraint.
Carlos M. Fonseca has included themes like Ranking, Decision theory and Artificial intelligence in his Mathematical optimization study.
While the research belongs to areas of Artificial intelligence, Carlos M. Fonseca spends his time largely on the problem of Machine learning, intersecting his research to questions surrounding Quantile.
His study looks at the intersection of Multi-objective optimization and topics like Quality control and genetic algorithms with Mating pool and Control engineering.
His work deals with themes such as Computational complexity theory and Real number, which intersect with Evolutionary algorithm.
His Evolutionary computation study combines topics from a wide range of disciplines, such as Dynamic programming, Fitness landscape, Genetic programming and Nonlinear system.
His most cited work include:
- Performance assessment of multiobjective optimizers: an analysis and review (2757 citations)
- An overview of evolutionary algorithms in multiobjective optimization (1956 citations)
- Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation (1060 citations)
What are the main themes of his work throughout his whole career to date?
Carlos M. Fonseca mainly investigates Mathematical optimization, Multi-objective optimization, Evolutionary algorithm, Artificial intelligence and Genetic algorithm.
His biological study spans a wide range of topics, including Time complexity and Selection.
Carlos M. Fonseca interconnects Probability distribution, Algorithm, Metaheuristic, Goal programming and Ranking in the investigation of issues within Multi-objective optimization.
The study incorporates disciplines such as Timetabling problem, Local search, Pareto principle and Theoretical computer science in addition to Evolutionary algorithm.
His Artificial intelligence research includes themes of Fitness landscape, Machine learning and Pattern recognition.
His work in Genetic algorithm covers topics such as Control system which are related to areas like Automatic control.
He most often published in these fields:
- Mathematical optimization (58.40%)
- Multi-objective optimization (36.00%)
- Evolutionary algorithm (29.60%)
What were the highlights of his more recent work (between 2014-2021)?
- Mathematical optimization (58.40%)
- Multi-objective optimization (36.00%)
- Combinatorics (16.00%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mathematical optimization, Multi-objective optimization, Combinatorics, Algorithm and Time complexity.
His work on Dynamic programming and Maximum flow problem as part of general Mathematical optimization research is frequently linked to Extension, Interdiction and Operational planning, thereby connecting diverse disciplines of science.
The various areas that Carlos M. Fonseca examines in his Multi-objective optimization study include Evolutionary algorithm, Multiple-criteria decision analysis and Selection.
Carlos M. Fonseca has researched Evolutionary algorithm in several fields, including Goal programming, Benchmark and Subject.
His work on Computation as part of general Algorithm study is frequently connected to Upper and lower bounds and Space, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His research on Time complexity also deals with topics like
- Asymptotically optimal algorithm which connect with Bayesian probability, Exact algorithm, Computational geometry and Probability distribution,
- Approximation algorithm that intertwine with fields like Maximization, Knapsack problem and Constraint.
Between 2014 and 2021, his most popular works were:
- Hypervolume subset selection in two dimensions: Formulations and algorithms (49 citations)
- A box decomposition algorithm to compute the hypervolume indicator (32 citations)
- On the Generalization Ability of Geometric Semantic Genetic Programming (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Mathematical optimization
His main research concerns Time complexity, Generalization, Mathematical optimization, Multi-objective optimization and Algorithm.
His Time complexity research incorporates elements of Binary logarithm, Submodular set function and Greedy algorithm.
His biological study focuses on Dynamic programming.
His research in the fields of Computation overlaps with other disciplines such as Upper and lower bounds and Selection.
His Genetic programming course of study focuses on Overfitting and Theoretical computer science.
His Semantics and Syntax study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Semantic search, bridging the gap between disciplines.
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