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H02D1A Genetic Algorithms and Evolutionary Computing Dirk Roose 1st semester ; 4 ECTS points Context: search and optimization H02D1A Genetic Algorithms and Evolutionary Computing 1st semester ; 4 ECTS points Aims t to describe genetic algorithms and other evolutionary strategies for search and optimisation t to analyse their performance (quality of results, cost) t to discuss some implementation issues t to illustrate the methods by solving some model problems (e.g. traveling salesman problem) t to present some case studies (e.g. concept learning, timetabling, ‘artificial life’) t the student will be able to decide whether these methods are suited to solve a particular search or optimisation problem, and how to choose appropriate genetic operators Genetic Algorithms and Evolutionary Computing Teaching activities • 10 lectures (Wed. at 9h) • 4 exercise & lab sessions (2 groups) Evaluation / exam • open book (theory + exercise) • short presentation of report on project Project • important part of the course • ‘classic’ form: genetic algorithm for traveling salesman problem; experiments with Matlab (?) code; in groups of 2 students ; 40 hours • for ambitious students: genetic algorithm for a chosen problem Prerequisite basic (bachelor) knowledge in programming, design of algorithms, and mathematics (analysis, statistics) Course Material – Book: Introduction to Evolutionary Computing (A. Eiben and J. Smith) Natural Computing Series, 2d edition, 2015, Springer (book ; e-book) – some papers