460-4086/02 – Biologically Inspired Algorithms (BIA)

The aim of the course is to deepen the basic knowledge of the modern computational methods derived from evolutionary and biological processes. Graduates will learn the eminent optimization problems and solve them using biologically inspired algorithms. Within the subject, mathematical methods will be mentioned briefly. At the end of the course, a student will be capable of applying an appropriate method to solve a specific optimization problem. Graduates will be capable of distinguishing between global and local optimization. He/she will learn the multiobjective and combinatorial optimization. The large-scale optimization will be mentioned. The graduate of the course will be able to: - define an optimization problem, - define evolutionary/swarm algorithm and local search algorithm, - distinguish among evolutionary algorithms, - distinguish among evolutionary algorithms, - solve optimization problem using a suitable biologically inspired algorithm, or choose a mathematical method, - identify problem-dependent variables and set them correctly in connection with the special problem, - suggest an appropriate method to speed up the optimization process.

The subject is focused on the biologically inspired algorithms used for optimization. Students will learn about the advantages and disadvantages of these methods compared to mathematical methods. They will be capable of distinguishing between evolutionary, swarm, and local algorithms and apply these algorithms to solve selected optimization problems. The course emphasizes both the diversity of optimization problems and the diversity of biologically inspired optimization techniques that are suitable for solving these problems. Students will then use the theoretical knowledge acquired in the lectures to complete practical tasks in the exercises. The exercises, therefore, closely correspond to the lectures. The aim of the course is to deepen the basic knowledge of the modern computational methods derived from evolutionary and biological processes. Graduates will learn the eminent optimization problems and solve them using biologically inspired algorithms. Within the subject, mathematical methods will be mentioned briefly. At the end of the course, a student will be capable of applying an appropriate method to solve a specific optimization problem. Graduates will be capable of distinguishing between global and local optimization. He/she will learn the multiobjective and combinatorial optimization. The large-scale optimization will be mentioned. The graduate of the course will be able to: - define an optimization problem, - define evolutionary/swarm algorithm and local search algorithm, - distinguish among evolutionary algorithms, - distinguish among evolutionary algorithms, - solve optimization problem using a suitable biologically inspired algorithm, or choose a mathematical method, - identify problem-dependent variables and set them correctly in connection with the special problem, - suggest an appropriate method to speed up the optimization process.

[1] Zelinka I., Oplatková Z., Šeda M., Ošmera P., Včelař F., Evoluční výpočetní techniky, principy a aplikace, BEN, 2008, Praha [2] Kvasnička V., Pospíchal J., Tiňo P., Evolučné algoritmy, STU Bralislava, ISBN 80-227-1377-5, 2000 3. Zelinka I., Včelař F., Čandík M., Fraktální geometrie – principy a aplikace, BEN, 2006, 160 p., ISBN 80-7300-191-8 [3] Back, T., Fogel, B., Michalewicz, Z.: Handbook of Evolutionary Computation, Institute of Physics, London [4] Davis L. 1996, Handbook of Genetic Algorithms, International Thomson Computer Press, ISBN 1850328250

The instructor checks the students' source codes in the exercises. Students demonstrate that they understand the optimization problems and algorithms, as well as their own source codes. Cheating is not tolerated. Repeated attempts to cheat can be a reason for non-award of credit points. The examination is oral.