This conference is aimed at exploring the intersections between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. The main purpose of the event is to bring together experts from these areas to discuss new ideas and methods, challenges and opportunities in various application areas, general trends and specific developments.
Topics of interest include, but are not limited to:
- Metaheuristics such as tabu search, iterated local search, evolutionary algorithms, memetic algorithms, ant colony optimization, and particle swarm optimization
- Hybridizations of metaheuristics with other techniques for optimization
- Supervised, unsupervised and reinforcement learning applied to heuristic search
- Reactive search optimization
- Self-adaptive algorithms
- Hyperheuristics
- Algorithm portfolios and off-line tuning methods
- Multiscale and multilevel methods
- Algorithms for dynamic, stochastic and multi-objective problems
- Interface(s) between discrete and continuous optimization
- Experimental analysis and modeling of algorithms
- Theoretical foundations
- Parallelization of optimization algorithms
- Memory-based optimization
- Software engineering of learning and intelligent optimization methods.
