Lectures:
- A brief introduction to the history of evolutionary algorithms and metaheuristics. Classification of metaheuristics based on their principle. Types of optimization problems (global, combinatorial, and multi-objective optimization). Objective function, constraints. Space of feasible solutions. Basic terminology.
- Local search algorithms: Hill climbing, tabu search, simulated annealing. Getting stuck in a local extreme and the methods of solution.
- Differential evolution: principle and its use in global optimization. Current variants of differential evolution and their application in real-world optimization tasks. Trends for improving the basic algorithms.
- Particle swarm and Self-Organizing Migration Algorithm (SOMA) and their current variants. Trends for improving basic algorithms.
- Combinatorial optimization: Knapsack, Traveling salesman problem, Vehicle routing problem. Solving selected problems using genetic algorithms.
- Combinatorial optimization and Ant colony optimization.
- Multi-objective optimization, Pareto set, Pareto frontier. NSGA II algorithm and its current variants.
- Optimization with constraints. Soft vs. hard constraints. Methods of the evaluation of the individual within the population. Nurse scheduling problem. Use of biologically inspired algorithms to solve selected problems.
- Evolutionary strategy: basic principle. Evolutionary strategy using covariance matrix (CMA-ES) in optimization with constraints.
- Dynamic optimization with constraints. Use of biologically inspired algorithms vs. other optimization methods.
- Optimization of problems with a high number of dimensions (large-scale optimization). The curse of dimensionality.
- Parallelization of biologically inspired algorithms.
- Comparing the algorithms from the perspective of their efficiency (statistical comparison of algorithms). No free lunch theorem.
Exercises (in PC classroom):
- Global optimization, selected problems. Common basis for biologically inspired algorithms. Individual, popupation, generation. Ways of the algorithm termination.
- Hill Climbing, Tabu Search, Simmulated annealing for the global optimization. The role of the normal and uniform distribution within the biologically inspired algorithms.
- Differential evolution and its enhanced versions applied on the global optimization problems.
- Particle Swarm Optimization algorithm and Self-organizing migrating algorithm (SOMA) and their application on the global optimization problems.
- Gennetic algorithm and combinatorial optimization ( Knapsack, Traveling Salesman Problem, Vehicle Routing Problem).
- Ant Colony Optimization and its application of the combinatorial optimization problems.
- Multiobjective optimization, NSGA II.
- Constrained optimization and biologically inspired algorithms. Nurse schedulling problem.
- CMA-ES and the constrained optmization.
- Dynamical optimization with constraints - application of the selected algorithms (differential evolution, SOMA) on the selected problems.
- Large scale optimization and the role of the biologically inspired algorithms.
- Parallelization of the biologically inspired algorithms.
- Statistical comparison of the biologically inspired algorithms.
- A brief introduction to the history of evolutionary algorithms and metaheuristics. Classification of metaheuristics based on their principle. Types of optimization problems (global, combinatorial, and multi-objective optimization). Objective function, constraints. Space of feasible solutions. Basic terminology.
- Local search algorithms: Hill climbing, tabu search, simulated annealing. Getting stuck in a local extreme and the methods of solution.
- Differential evolution: principle and its use in global optimization. Current variants of differential evolution and their application in real-world optimization tasks. Trends for improving the basic algorithms.
- Particle swarm and Self-Organizing Migration Algorithm (SOMA) and their current variants. Trends for improving basic algorithms.
- Combinatorial optimization: Knapsack, Traveling salesman problem, Vehicle routing problem. Solving selected problems using genetic algorithms.
- Combinatorial optimization and Ant colony optimization.
- Multi-objective optimization, Pareto set, Pareto frontier. NSGA II algorithm and its current variants.
- Optimization with constraints. Soft vs. hard constraints. Methods of the evaluation of the individual within the population. Nurse scheduling problem. Use of biologically inspired algorithms to solve selected problems.
- Evolutionary strategy: basic principle. Evolutionary strategy using covariance matrix (CMA-ES) in optimization with constraints.
- Dynamic optimization with constraints. Use of biologically inspired algorithms vs. other optimization methods.
- Optimization of problems with a high number of dimensions (large-scale optimization). The curse of dimensionality.
- Parallelization of biologically inspired algorithms.
- Comparing the algorithms from the perspective of their efficiency (statistical comparison of algorithms). No free lunch theorem.
Exercises (in PC classroom):
- Global optimization, selected problems. Common basis for biologically inspired algorithms. Individual, popupation, generation. Ways of the algorithm termination.
- Hill Climbing, Tabu Search, Simmulated annealing for the global optimization. The role of the normal and uniform distribution within the biologically inspired algorithms.
- Differential evolution and its enhanced versions applied on the global optimization problems.
- Particle Swarm Optimization algorithm and Self-organizing migrating algorithm (SOMA) and their application on the global optimization problems.
- Gennetic algorithm and combinatorial optimization ( Knapsack, Traveling Salesman Problem, Vehicle Routing Problem).
- Ant Colony Optimization and its application of the combinatorial optimization problems.
- Multiobjective optimization, NSGA II.
- Constrained optimization and biologically inspired algorithms. Nurse schedulling problem.
- CMA-ES and the constrained optmization.
- Dynamical optimization with constraints - application of the selected algorithms (differential evolution, SOMA) on the selected problems.
- Large scale optimization and the role of the biologically inspired algorithms.
- Parallelization of the biologically inspired algorithms.
- Statistical comparison of the biologically inspired algorithms.