An Overview of Evolutionary Multi-Objective Optimization

Table of Contents

Seminars-2-2024 - This article is part of a series.

Part 1: The Dawn of an Immersive Internet: XR, Generative AI and the Road to 6G

Part 2: AGI Chips - The Next Frontier

Part 3: Generalist vs Specialist Language Models

Part 4: From Intelligent Surfaces to Noise-Driven Communication: Innovative Technologies for 6G and Beyond

Part 5: This Article

Part 6: Packet Trimming at the Edge for Low Latency in 6G Environments

Part 7: Scientific Machine Learning and Quantum Utility: A Near Future Perspective

Part 8: Cloud Storage Systems: Latency Characterization and Extensions

Part 9: Let the Plants do the Talking: Climate-Smart Agriculture by the Messages Received from Plants and Soil

Part 10: More is different: How the Science of Complex Systems can inspire Future Autonomous Networks

Abstract #

Multi-objective optimization refers to solving problems having two or more (often conflicting) objectives at the same time. Such problems are ill-defined and their solution is not a single solution but instead, a set of them, which represent the best possible trade-offs among the objectives. Evolutionary algorithms are particularly suitable for solving multi-objective problems because they are population-based, and require little domain-specific information to conduct the search. Due to these advantages, the development of the so-called multi-objective evolutionary algorithms (MOEAs) has significantly increased in the last 15 years. In this talk, we will provide a general overview of the field, including the main algorithms in current use as well as some of the many applications of them.

Bio of Carlos Artemio Coello Coello #

Carlos Artemio Coello Coello received a PhD in Computer Science from Tulane University (USA) in 1996. His research has mainly focused on the design of new multi-objective optimization algorithms based on bio-inspired metaheuristics (e.g., evolutionary algorithms), which is an area in which he has made pioneering contributions. He has received several awards, including the National Research Award (in 2007) from the Mexican Academy of Science and the 2012 National Medal of Science in Physics, Mathematics and Natural Sciences from Mexico’s presidency, the 2013 IEEE Kiyo Tomiyasu Award, the 2016 The World Academy of Sciences (TWAS) Award in “Engineering Sciences”, and the 2021 IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award. Since January 2011, he is an IEEE Fellow. He is currently the Editor-in-Chief of the IEEE Transactions on Evolutionary Computation. He is Full Professor with distinction (Investigador Cinvestav 3F) at the Computer Science Department of CINVESTAV-IPN in Mexico City, Mexico.