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.