At the end of the course, the students will be able to

1.1. learn basic types of conventional construction and improvement heuristic algorithms.

1.2. comprehend computational complexity and understand empirical performance of conventional heuristic algorithms.

2.1. learn basic principles and operators of metaheuristics such as simulated annealing, tabu search, evolutionary algorithms, and swarm intelligence.

2.2. develop and implement a metaheuristic search algorithm for an optimization problem of their choice.

3.1. use design of experiments to fine tune a metaheuristic by adjusting the algorithm and problem parameters.

3.2. evaluate the performance of a metaheuristic search algorithm empirically and compare it with its competitors.