Heuristic algorithms Basic Information I072 (2+2+0) - 6 ECTS credits Students will be introduced to heuristic algorithms which in application find good enough solutions for problems which are too complex to solve it exactly. Students will learn to distinguish concepts of heuristics, metaheuristics and hyperheuristics. Students will study and analyse the most known metaheuristics like Tabu Search, Genetic and Evolutionary Algorithms and Ant Colony optimization algorithm. Students will have an insight into the real-world optimization problems that are solved by these algorithms. Through the implementation of metaheuristic approaches, students will learn how to select the parameters of algorithms which play an essential role in finding good solutions. You can access the course content at the following link: PDF Teachers Basic literature J. Dréo, A. Pétrowski, P. Siarry, E. Taillard, Metaheuristics for Hard Optimization: Methods and Case Studies, Springer, 2005. E. G. Talbi, Metaheuristics: From Design to Implementation, WIley, 2009. Additional literature X. S.Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2008. Z. Michalewicz, D. B. Fogel, How to Solve it: Modern Heuristics, 2nd Edition, Springer-Verlag, 2004. M. Čupić, B. Dalbelo Bašić, M. Golub, Neizrazito, evolucijsko i neuroračunarstvo, Sveučilište u Zagrebu, Fakultet elektrotehnike i računarstva, 2012. J. Hromkovič, Algorithmics for Hard Problems, 2nd edition, Springer, 2003. Teaching materials The materials are available on the internal Teams channel of the course, through which all internal communication takes place. Students are required to register on the course’s Teams channel. The channel code for joining the course can be found in the schedule.