2023 - Research.com Computer Science in Mexico Leader Award
2022 - Research.com Computer Science in Mexico Leader Award
2013 - IEEE Kiyo Tomiyasu Award “For pioneering contributions to single and multiobjective optimization techniques using bioinspired metaheuristics.”
2011 - IEEE Fellow For contributions to multi-objective optimization and constraint-handling techniques
His primary scientific interests are in Mathematical optimization, Multi-objective optimization, Evolutionary algorithm, Metaheuristic and Evolutionary computation. His study involves Genetic algorithm, Multi-swarm optimization, Pareto principle, Meta-optimization and Particle swarm optimization, a branch of Mathematical optimization. The concepts of his Multi-objective optimization study are interwoven with issues in Test functions for optimization, Metric, Fitness function, Vector optimization and Decision theory.
His Evolutionary algorithm research incorporates elements of Algorithm, Penalty method and Management science. His research integrates issues of Optimization problem and Engineering optimization in his study of Metaheuristic. His research in Evolutionary computation tackles topics such as Memetic algorithm which are related to areas like Java Evolutionary Computation Toolkit.
The scientist’s investigation covers issues in Mathematical optimization, Evolutionary algorithm, Multi-objective optimization, Optimization problem and Evolutionary computation. Carlos A. Coello Coello has researched Mathematical optimization in several fields, including Algorithm and Set. His research in Evolutionary algorithm intersects with topics in Differential evolution and Fitness function.
The Multi-objective optimization study which covers Genetic algorithm that intersects with Combinational logic and Crossover. His work carried out in the field of Evolutionary computation brings together such families of science as Linear programming, Algorithm design, Theoretical computer science and Management science. His Metaheuristic study integrates concerns from other disciplines, such as Continuous optimization and Test functions for optimization.
His scientific interests lie mostly in Evolutionary algorithm, Mathematical optimization, Optimization problem, Multi-objective optimization and Evolutionary computation. His Evolutionary algorithm study deals with the bigger picture of Artificial intelligence. His Mathematical optimization research incorporates themes from Set and Selection.
His Optimization problem research is multidisciplinary, incorporating elements of Performance indicator, Boundary, Decomposition and Heuristics. His Multi-objective optimization research integrates issues from Space, Field, Estimator and Operator. His study in the field of Evolutionary programming also crosses realms of Electronic mail.
Evolutionary algorithm, Mathematical optimization, Multi-objective optimization, Optimization problem and Evolutionary computation are his primary areas of study. The Evolutionary algorithm study combines topics in areas such as Sorting, Pareto principle and Computational intelligence. Carlos A. Coello Coello has included themes like Convergence and Selection in his Mathematical optimization study.
His Multi-objective optimization research includes themes of Field, Set and Evolution strategy. His research in Optimization problem intersects with topics in Ant colony optimization algorithms, Decomposition, Heuristics and Combinatorics. He combines subjects such as Linear programming and Theoretical computer science with his study of Evolutionary computation.
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.
Handling multiple objectives with particle swarm optimization
C.A.C. Coello;G.T. Pulido;M.S. Lechuga.
IEEE Transactions on Evolutionary Computation (2004)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Carlos A. Coello Coello;Gary B. Lamont;David A. Van Veldhuizen.
THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART
Carlos A Coello Coello.
Computer Methods in Applied Mechanics and Engineering (2002)
MOPSO: a proposal for multiple objective particle swarm optimization
C.A. Coello Coello;M.S. Lechuga.
congress on evolutionary computation (2002)
A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques
Carlos A. Coello Coello.
Knowledge and Information Systems (1999)
Evolutionary multi-objective optimization: a historical view of the field
C.A. Coello Coello.
IEEE Computational Intelligence Magazine (2006)
Use of a self-adaptive penalty approach for engineering optimization problems
Carlos A. Coello Coello.
Computers in Industry (2000)
An updated survey of GA-based multiobjective optimization techniques
Carlos A. Coello.
ACM Computing Surveys (2000)
Constraint-Handling in Nature-Inspired Numerical Optimization: Past, Present and Future
Efrén Mezura-Montes;Carlos A. Coello Coello.
Swarm and evolutionary computation (2011)
Constraint-handling in genetic algorithms through the use of dominance-based tournament selection
Carlos A. Coello Coello;Efrén Mezura Montes.
Advanced Engineering Informatics (2002)
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