Carlos Coello

Dr. Carlos Coello is Professor with Distinction (Investigador CINVESTAV 3F) at the Center for Research and Advanced Studies of the Instituto Politécnico Nacional (CINVESTAV-IPN) in Mexico City and Visiting Professor at the Basque Center for Applied Mathematics in Spain. Has taught master's and Ph.D. level courses on evolutionary computation, evolutionary multi-objective optimization, programming languages, and engineering optimization at CINVESTAV-IPN. In addition, he has taught short courses in Spain, England, Argentina, Chile, India, Bolivia, Colombia, Slovenia, and the USA.

Also, Professor Coello has presented scientific articles at major conferences specialized in evolutionary computation, like the IEEE Congress on Evolutionary Computation (CEC), the Genetic and Evolutionary Computation Conference (GECCO), Parallel Problem Solving from Nature (PPSN), and others. 

His work and research fall at the intersection of computer science, applied mathematics, and operations research. His main contributions have revolved around the design of biologically inspired stochastic algorithms to solve highly complex multi-objective optimization problems (mainly non-linear). He has made pioneering contributions to this area which is now known as multi-objective evolutionary optimization. For example, he proposed, along with his research group, the first genetic micro-algorithm for multi-objective optimization, which has been used in real-world applications in various countries like the United States for the design of supersonic business jets. Also, the first algorithm for multi-objective optimization based on an artificial immune system incorporating the concept of Pareto optimality, which has been a reference in specialized literature used to validate new multi-objective algorithms.

Dr. Coello has broad experience as an expert consultant in his field. He is a Member of the Foundation Advisory Board of The International AIQT Foundation, which strives to establish a highly competitive (international-level) research center in artificial intelligence and quantum technology. He is also Scientific Advisor of the company Complexica, in Australia, and was Senior Advisor of the Hunan Zixing AI Research Institute from China (2017-2020), to name some examples.

His research interests revolve around the areas of evolutionary computation (genetic algorithms and evolution strategies), as well as engineering optimization. In addition, he has worked on over 12 funded research projects, the most recent one called ‘Alternative Selection Schemes for Multi-Objective Evolutionary Algorithms’ funded by CONACYT, in which he was Principal Investigator.

Dr. Coello has collaborated as an Associate Editor in multiple international journals like the IEEE Transactions on Evolutionary Computation, Evolutionary Computation (the MIT Press), Journal of Heuristics (Springer), Computational Optimization and Applications (Springer), Pattern Analysis and Applications (Springer), to name a few. He is currently Editor-in-Chief of the IEEE Transactions on Evolutionary Computation journal.

Among his multiple special recognitions and awards, Dr. Carlos Coello won the Premio Crónica 2023 in Science and Technology; he was selected by Líderes magazine as part of their "300 most influential leaders in Mexico" list in 2023, taking the 69th position. He also won the SIGEVO Outstanding Contribution Award (2023) given by the Association for Computing Machinery (ACM); the IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2021); The World Academy of Sciences (TWAS) Award in Engineering Sciences (2016); the Best Paper Award from the Evolutionary Multi-objective Optimization track (GECCO’2016); the IEEE Kiyo Tomiyasu Award (2013); the National Medal of Science and Arts in the area of Physical, Mathematical and Natural Sciences (2012), which is the highest award a Mexican scientist can obtain in the country, granted by the Presidency of Mexico; the Scopus Award (Mexico's edition; 2012) in Engineering for being the most cited scientist in engineering in the 5 years previous to the award; the Medal to the Scientific Merit from Mexico City's congress (2009); among many others. 

Dr. Coello is level 3 in the National System of Researchers (SNI) in Mexico and was also ranked #289 in the world and #1 in Mexico in the 8th Edition of the Guide2Research 2022 Ranking of Top 1000 Scientists in the field of Computer Science and Electronics.

Professor Carlos Coello joined Tecnológico de Monterrey as Distinguished Visiting Professor in Computer Science and Computational Intelligence for the School of Engineering and Sciences.

• Ph.D., Computer Science, Tulane University, New Orleans, Louisiana, USA.
• M.Sc., Computer Science, Tulane University, New Orleans, Louisiana, USA.
• B.Sc., Civil Engineering, Universidad Autónoma de Chiapas, Mexico. (Summa cum laude).
   

• Sofien Boutaib, Maha Elarbi, Slim Bechikh, Carlos A. Coello Coello, Lamjed Ben Said, Uncertainty-wise Software Anti-patterns Detection: A Possibilistic Evolutionary Machine Learning Approach, Applied Soft Computing, Vol. 129, Article Number: 109620, November 2022.

• Forhad Zaman, Saber Elsayed, Ruhul Sarker, Daryl Essam and Carlos A. Coello Coello, Pro-Reactive Approach for Project Scheduling Under Unpredictable Disruptions, IEEE Transactions on Cybernetics, Vol. 52, No. 11, pp. 11299--11312, November 2022.

• Lingjie Li, Yongfeng Li, Qiuzhen Lin, Zhong Ming, Carlos A. Coello Coello, A Convergence and Diversity Guided Leader Selection Strategy for Many-objective Particle Swarm Optimization<, Engineering Applications of Artificial Intelligence, Vol. 155, Article Number: 105249, October 2022.

• Qiuzhen Lin, Xunfeng Wu, Jianqiang Li, Maoguo Gong and Carlos A. Coello Coello, An Ensemble Surrogate-based Framework for Expensive Multiobjective Evolutionary Optimization, IEEE Transactions on Evolutionary Computation, Vol. 26, No. 4, pp. 631--645, August 2022.

• Qiyuan Yu, Qiuzhen Lin, Zexuan Zhu, Ka-Chun Wong, Carlos A. Coello Coello, A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering, Swarm and Evolutionary Computation, Vol. 71, Article No. 101075, June 2022.

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