L4EC - Learning for Evolutionary Computation

Description

Learning for Evolutionary Computation (L4EC) is a relatively new track introduced in 2024 to recognize high-quality research that uses machine learning or statistical techniques and concepts to improve heuristics and algorithmic components in the field of evolutionary computation (EC).

Scope

This track focuses on heuristics, methods and concepts that leverage machine learning (including deep learning and reinforcement learning) or statistics to enhance EC methods. As such, topics of interest include, but are not limited to:

• Methods for automated algorithm design, selection, and configuration,
• Mechanisms that learn how to control or coordinate a set of EC algorithms, such as parameter tuning, parameter control, dynamic algorithm selection/configuration, and meta-heuristics,
• EC algorithms integrating methods to extract knowledge from the population dynamics, search trajectory and/or the genotype,
• Surrogate-based or surrogate-assisted optimization of expensive fitness functions, including multi-fidelity approaches,
• Feature-based methods that learn to characterize optimization problems, such as exploratory landscape analysis (ELA) and fitness landscape analysis.

In focusing on the use of learning methods for EC, this track complements the existing EML track, which focuses on the use of EC for machine learning problems.