Tutorials
The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.
Tutorial proposals are accepted until:
September 11, 2025
If you wish to propose a new Tutorial please kindly fill out and submit this
Expression of Interest form.
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Tutorial on
Evolutionary Computation, Tunneling and Connections to Quantum Computing
Instructor
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Darrell Whitley
Colorado State University
United States
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Brief Bio
Prof. Darrell Whitley has been active in Evolutionary Computation since 1986, and has published more than 250 papers. These papers have garnered more than 33,000 citations. Dr. Whitley’s H-index is 73. He introduced the first “steady state genetic algorithm” with rank based selection, and has worked on dozens of real world applications of evolutionary algorithms. He has served as Editor-in-Chief of the journal Evolutionary Computation, and served as Chair of the Governing Board of ACM Sigevo from 2007 to 2011. He is a Fellow of the ACM recognized for his contributions to Evolutionary Computation, and he was awarded the 2022 IEEE PIONEER Award in Evolutionary Computation.
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Abstract
This tutorial is mainly about Evolutionary Algorithms, but it will highlight ways in which Evolutionary Algorithms and Quantum Computing share the ability to efficiently tunnel between local optima and to exploit problem representations with low nonlinearity. New theoretical results offer new insights to explain why “Partition Crossover” is so successful at tunneling between local optima on NP Hard problems such as MAX-kSAT and the Traveling Salesman Problem. Partition Crossover can also be applied to Quadratic Unconstrained Boolean Optimization (QUBO) problems, which exploit a keystone representation in Quantum Optimization. The tutorial will briefly review key basic concepts from Quantum Computing. Problem transforms that are used in Quantum Computing to reduce problem nonlinearity also offer critical advantages for Evolutionary Algorithms due to the fact that powerful evolutionary operators can also exploit problem representations with low nonlinearity.