The FOGA series aims at advancing our understanding of the working principles behind evolutionary algorithms and related randomized search heuristics, such as local search algorithms, differential evolution, ant colony optimization, particle swarm optimization, artificial immune systems, simulated annealing, and other Monte Carlo methods for search and optimization. Connections to related areas, such as Bayesian optimization and direct search, are of interest as well. FOGA is the premier event to discuss advances on the theoretical foundations of these algorithms, tools needed to analyze them, and different aspects of comparing algorithms’ performance. Topics of interest include, but are not limited to:

    Run time analysis
    Mathematical tools suitable for the analysis of search heuristics
    Fitness landscapes and problem difficulty
    (On- and offline) configuration and selection of algorithms, heuristics, operators, and parameters
    Stochastic and dynamic environments, noisy evaluations
    Constrained optimization
    Problem representation
    Complexity theory for search heuristics
    Multi-objective optimization
    Benchmarking aspects, including performance measures, the selection of meaningful benchmark problems, and statistical aspects
    Connection between black-box optimization and machine learning

Submissions covering the entire spectrum of work, ranging from rigorously derived mathematical results to carefully crafted empirical studies, are invited.