Autonomous Search
Decades of innovations in combinatorial problem solving have produced better and more complex algorithms. These new methods are better since they can solve larger problems and address new application domains. They are also more complex which means that they are hard to reproduce and often harder to...
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| Other Authors: | , , |
| Format: | Electronic eBook |
| Language: | English |
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Berlin, Heidelberg :
Springer Berlin Heidelberg,
2012.
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| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- An Introduction to Autonomous Search.-Part I – Offline Configuration.-Evolutionary Algorithm Parameters and Methods to Tune Them
- Automated Algorithm Configuration and Parameter Tuning
- Case-Based Reasoning for Autonomous Constraint Solving
- Learning a Mixture of Search Heuristics
- Part II – Online Control
- An Investigation of Reinforcement Learning for Reactive Search Optimization
- Adaptive Operator Selection and Management in Evolutionary Algorithms
- Parameter Adaptation in Ant Colony Optimization
- Part III – New Directions and Applications
- Continuous Search in Constraint Programming
- Control-Based Clause Sharing in Parallel SAT Solving
- Learning Feature-Based Heuristic Functions.
