Genetic algorithms

Objectives and outcomes

Understanding the place of genetic algorithms among traditional and new optimisation methods. Learning how to use binary and continuous genetic algorithms. Learning about the most important paradigmatic applications of binary and continuous genetic algorithms. Upon completion of the course, students understand genetic algorithms. They can solve problems using the methods of genetic algorithms.


Development and application of genetic algorithms. Description of a simple genetic algorithm. A method of solving problems using genetic algorithms – coding. Genetic operators: crossover, mutation, selection. Various modifications of genetic algorithms depending on the type of operators used. Theoretical bases of genetic algorithms. Scheme theorem. Areas and methods of application of genetic algorithms. Parallel genetic algorithms. Comparison of genetic algorithms and other heuristic methods for solving optimisation problems.

Practical classes

Implementation of the algorithms covered in lectures and solving specific problems using genetic algorithms. Application of binary and continuous genetic algorithms. Optimisation of multiple objectives. Hybrid genetic algorithms. Selection of parameters. Parallel genetic algorithms. Application to the travelling salesman problem. Application in decoding.