Submission deadline: January 31, 2013 ====================================================================
Search and optimization problems are everywhere, and search algorithms are getting increasingly powerful. However, they are also getting increasingly complex, and only autonomous self-managed systems that provide high-level abstractions can turn search algorithms into widely used methodologies. Such systems should be able to configure themselves on the fly, automatically adapting to the changing problem conditions, based on general goals provided by their users. The overall goal is to reduce the role of the human expert in the process of designing an algorithm to solve a computational search problem.
The aim of the Self-* Search track is to bring together researchers from computer science, artificial intelligence and operations research, interested in software systems able to automatically tune, configure, or even generate and design optimization algorithms and search heuristics. We also encourage submissions related to the automated design and configuration of algorithms in other areas such as machine learning, constraint programming and games.
We invite all papers related to Self-* Search, in particular (but not limited to) those in the following subject areas:
* Adaptive differential evolution and particle swarm optimisation * Adaptive and co-evolutionary multimeme algorithms * Adaptive and self-adaptive parameter control * Adaptive operator selection * Algorithm selection and portfolios * Applications of self-* techniques to multi-objective, dynamic, and complex real-world problems. * Auto-constructive evolution * Automated construction of heuristics and/or algorithms * Automatic algorithm configuration * Autonomous control for search algorithms * Computer-aided algorithm design * Cross-domain heuristic search * Evolving heuristics and/or algorithms * Hyper-heuristics * Meta-learning and meta-genetic programming * Multi-level search * Online learning for heuristic/operator selection * Reactive search and intelligent optimization