In this course we focus on search and optimization techniques for solving AI problems and knowledge representation dealing with uncertain.
We study general high-level procedures (metaheuristics) that are among the most effective solution strategies for solving combinatorial optimization problems in practice. They are designed to solve large-scale optimization problems that cannot be solved in reasonable processing time by the classic combinatorial optimization methods.
On the other hand, we present the ability of fuzzy rule-based systems to deal with unknown or incompletely specified environments and their applications in real world problems.
- Elisa Guerrero Vazquez (email@example.com)
- Andrés Yáñez Escolano
- María de la Paz Guerrero Lebrero
- Guillermo Bárcena González
Computer Sciences Department (University of Cadiz)
Metaheuristics, Simulated Annealing, Tabu Search, Constraint Satisfaction Problems, Genetic Algorithms, Particle Swarm Optimization, Fuzzy Systems, Mandani Inference
Advanced techniques in Search and Optimization
1. Constraint Satisfaction Problems
2. Metaheuristics based on trajectories
3. Metaheuristics based on populations
4. Fuzzy Systems
- Know the basics of Intelligent Systems through the description and identification of optimization and search problems.
- Define constraint satisfaction problems, and study techniques and metaheuristics for their resolution.
- Know and apply the main metaheuristics based on trajectories, such as simulated annealing and taboo search.
- Know and apply the main population-based metaheuristics, such as genetic algorithms and particle clusters.
- Know different approaches for managing uncertainty such as Fuzzy systems.