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GECH - General Evolutionary Computation and Hybrids

Description

General Evolutionary Computation and Hybrids is a track focusing on how EAs are used as part of larger systems in synergy with other algorithms, including hybrid methods and other, more general combinations of EAs with other components. We also welcome high-quality contributions on a wide range of EA topics which do not fit exclusively into other GECCO tracks. We don’t consider hybrids based only on superficial metaphors (Sörensen, 2015) as on-topic for this track.

Scope

Areas of interest include the following - but the limit should be set by your creativity not ours:

  • Combining EAs with mechanisms to control or coordinate a set of algorithms, such as hyper-heuristics (selective and generative);
  • Combining EAs with constructive heuristics;
  • Combining EAs with classical methods (linear and integer programming, dynamic programming, constraint programming, etc.);
  • Combining EAs and traditional AI methods such as A-star, tree search, Monte Carlo tree search;
  • EAs incorporating multi-fidelity and multi-resolution objective function evaluation techniques;
  • Hybridising approaches such as EA+EA (e.g., meta-EA), EA+PSO, EA+ACO, EA+LS (memetic), EA+Fuzzy;
  • EA+A-life including co-evolutionary methods, both competitive and co-operative;
  • Search algorithms combining quantum and classical computation;
  • EAs using special techniques for parallel and distributed computing, or high performance hardware such as GPUs;
  • Hybrid EAs which use landscape analysis techniques as part of the search.

Track Chairs

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Emma Hart

Edinburgh Napier University

Prof. Hart gained a 1st Class Honours Degree in Chemistry from the University of Oxford, followed by an MSc in Artificial Intelligence from the University of Edinburgh. Her PhD, also from the University of Edinburgh, explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimisation and data classification problems. She moved to Edinburgh Napier University in 2000 as a lecturer, and was promoted to a Chair in 2008 where she leads a group in Nature-Inspired Intelligent Systems, specialising in optimisation and learning algorithms applied in domains that range from combinatorial optimisation to robotics. Her work mainly involves development of algorithms inspired by biological evolution to discover novel solutions to challenging problems. She was appointed as Editor-in-Chief of Evolutionary Computation (MIT Press) in 2017. She has been invited to give keynotes at major international conferences including CLAIO 2020, IEEE CEC 2019, EURO 2016 and UKCI 2015 and was General Chair of PPSN 2016, and as a Track Chair at GECCO for several years. She is an elected member of the Executive Board of the ACM SIG on Evolutionary Computation. More broadly, she invited member of the UK Operations Research Society Research Panel, and in Scotland, co-leads the Artificial Intelligence theme within SICSA. She was appointed as a panel member for REF2021 (UoA11 Computer Science). In 2020 she was appointed to the Steering Committee that developed Scotland's AI Strategy published in 2021 . She has a sustained track record of obtaining funding from the EU, EPSRC and of engaging with industry via KTP projects and consultancy, and participates enthusiastically in public-engagement activity, e.g Pint of Science. Her work in evolutionary robotics has attracted significant media attention, e.g. in New Scientist, the Guardian, Telegraph and the Conversation. In 2021, she gave a TED Talk on Evolutionary Robotics, available online

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Gisele L. Pappa

UFMG, Brazil | webpage

Gisele Pappa is an Associate Professor in the Computer Science Department at UFMG, Brazil. Her main research interests are the intersection of the areas of machine learning and evolutionary computation, with a special interest in genetic programming and its applications in classification and regression tasks. She has also been actively researching the use of EAs for automated machine learning, focusing on applications for health data and also fraud detection.