What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mathematical optimization
- Algorithm
Kalyanmoy Deb mainly investigates Mathematical optimization, Multi-objective optimization, Evolutionary algorithm, Optimization problem and Genetic algorithm.
Kalyanmoy Deb is interested in Evolutionary computation, which is a field of Mathematical optimization.
His Multi-objective optimization research incorporates themes from Multi-swarm optimization, Probabilistic-based design optimization and Metaheuristic, Artificial intelligence.
He focuses mostly in the field of Evolutionary algorithm, narrowing it down to matters related to Computational complexity theory and, in some cases, Evolution strategy.
His work in Optimization problem addresses subjects such as Engineering design process, which are connected to disciplines such as Multiple-criteria decision analysis.
His work in Genetic algorithm covers topics such as Test functions for optimization which are related to areas like Field.
His most cited work include:
- A fast and elitist multiobjective genetic algorithm: NSGA-II (25804 citations)
- Multi-Objective Optimization Using Evolutionary Algorithms (11432 citations)
- Muiltiobjective optimization using nondominated sorting in genetic algorithms (4978 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Mathematical optimization, Multi-objective optimization, Evolutionary algorithm, Optimization problem and Genetic algorithm.
The Mathematical optimization study combines topics in areas such as Algorithm and Set.
His work deals with themes such as Multi-swarm optimization, Engineering optimization, Probabilistic-based design optimization and Artificial intelligence, which intersect with Multi-objective optimization.
His Evolutionary programming study in the realm of Evolutionary algorithm interacts with subjects such as Scalability.
His work is dedicated to discovering how Optimization problem, Constrained optimization are connected with Penalty method and other disciplines.
The study incorporates disciplines such as Sorting and Crossover in addition to Genetic algorithm.
He most often published in these fields:
- Mathematical optimization (64.33%)
- Multi-objective optimization (42.66%)
- Evolutionary algorithm (36.74%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical optimization (64.33%)
- Multi-objective optimization (42.66%)
- Optimization problem (34.32%)
In recent papers he was focusing on the following fields of study:
Mathematical optimization, Multi-objective optimization, Optimization problem, Evolutionary algorithm and Artificial intelligence are his primary areas of study.
Kalyanmoy Deb regularly ties together related areas like Set in his Mathematical optimization studies.
Kalyanmoy Deb has researched Multi-objective optimization in several fields, including Metaheuristic, Metric, Pareto principle, Boundary and Function.
His Optimization problem study incorporates themes from Local search, Set, Constrained optimization and Metamodeling.
His studies in Evolutionary algorithm integrate themes in fields like Selection, Benchmark, Algorithm, Computation and Convolutional neural network.
His Genetic algorithm study combines topics from a wide range of disciplines, such as Sorting, Crossover and Search algorithm.
Between 2016 and 2021, his most popular works were:
- A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications (255 citations)
- Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications (103 citations)
- NSGA-Net: neural architecture search using multi-objective genetic algorithm (93 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mathematical optimization
- Algorithm
Kalyanmoy Deb spends much of his time researching Mathematical optimization, Multi-objective optimization, Evolutionary algorithm, Optimization problem and Artificial intelligence.
His biological study focuses on Evolutionary computation.
His work carried out in the field of Multi-objective optimization brings together such families of science as Process, Metaheuristic, Genetic algorithm, Pareto principle and Cluster analysis.
His biological study spans a wide range of topics, including Bilevel optimization, Set, Structure, Benchmark and Convolutional neural network.
His work carried out in the field of Optimization problem brings together such families of science as Linear programming, Global optimization, Karush–Kuhn–Tucker conditions and Kriging.
Kalyanmoy Deb combines subjects such as Machine learning and Data mining with his study of Artificial intelligence.
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