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The goal of the lecture is to introduce the main nature-inspired algorithms (evolutionary algorithm, neural
networks, …) and how they can be applied to solve problems in optimization and machine learning. In the
seminar, some of the algorithms will be implemented and used to solve simple problems in the areas mentioned
above.
Last update: Töpfer Pavel, doc. RNDr., CSc. (30.01.2018)
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In order to pass the course, the student must obtain the credit for the seminar and pass an exam. The credit is given for solving assignments from the seminar. The nature of study verification excludes the possibility of its repetition. The exam is oral with time for written preparation. The requirements correspond to the syllabus in the extent presented during the lectures. A part of the exam may be the design of an algorithm for a given problem. Last update: Töpfer Pavel, doc. RNDr., CSc. (30.01.2018)
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Olarius S., Zomaya A. Y., Handbook of Bioinspired Algorithms and Applications, Chapman & Hall/CRC, 2005. ISBN: 978-1-584-88475-0
de Castro, L. N., Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications, CRC Press, 2006. ISBN: 978-1-584-88643-3
Eiben, A.E and Smith, J.E.: Introduction to Evolutionary Computing, (2nd ed), Springer-Verlag, 2015. ISBN: 978-3-662-44874-8
Poli R., Langdon W. B., McPhee, N. F., A Field Guide to Genetic Programming. Lulu.com, 2008 ISBN: 978-1-409-20073-4
Bengio Y., Goodfellow I. J., Courville A., Deep Learning. MIT Press, 2016. ISBN: 978-0-262-03561-3
Last update: Töpfer Pavel, doc. RNDr., CSc. (30.01.2018)
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Evolutionary models Neural models
Simple genetic algorithm Representation, genetic operators, fitness, selection Evolutionary algorithms for continuous optimization Neuro-evolution, algorithm NEAT Genetic programming
Ant Colony Optimization Particle Swarm Optimization
Perceptron, multi-layered perceptron, back-propagation as a learning algorithm Convolutional networks RBF networks a Kohonen’s maps
Artificial Immune Systems Cellular Automata Artificial Life
Continuous and combinatorial optimization Multi-objective optimization Supervised and unsupervised learning, reinforcement learning Last update: Töpfer Pavel, doc. RNDr., CSc. (30.01.2018)
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