2023 - Research.com Computer Science in United States Leader Award
2018 - Evolutionary Computation Pioneer Award, IEEE Computational Intelligence Society
2011 - ACM Senior Member
Fellow of the Indian National Academy of Engineering (INAE)
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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A fast and elitist multiobjective genetic algorithm: NSGA-II
K. Deb;A. Pratap;S. Agarwal;T. Meyarivan.
IEEE Transactions on Evolutionary Computation (2002)
Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction
Multi-objective Evolutionary Optimisation for Product Design and Manufacturing (2011)
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb;Deb Kalyanmoy.
Muiltiobjective optimization using nondominated sorting in genetic algorithms
N. Srinivas;Kalyanmoy Deb.
Evolutionary Computation (1994)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Eckart Zitzler;Kalyanmoy Deb;Lothar Thiele.
Evolutionary Computation (2000)
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
Kalyanmoy Deb;Samir Agrawal;Amrit Pratap;T. Meyarivan.
parallel problem solving from nature (2000)
An efficient constraint handling method for genetic algorithms
Computer Methods in Applied Mechanics and Engineering (2000)
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb;Himanshu Jain.
IEEE Transactions on Evolutionary Computation (2014)
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
David E. Goldberg;Kalyanmoy Deb.
foundations of genetic algorithms (1991)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: