9600-0009/02 – Unconvential Algorithms and Computations (NAV)
| Gurantor department | IT4Innovations | Credits | 10 |
| Subject guarantor | prof. Ing. Ivan Zelinka, Ph.D. | Subject version guarantor | prof. Ing. Ivan Zelinka, Ph.D. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2015/2016 | Year of cancellation | |
| Intended for the faculties | FEI, USP | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
Upon the successful completion of the course, graduates will acquire interdisciplinary knowledge of unconventional algorithms and will be able to apply the discussed methods to practical problems. The graduates are expected to be able to continue in further deeper studies of this field on their own.
Teaching methods
Lectures
Individual consultations
Summary
The aim of the subject is to introduce students to the area of unconventional algorithms and their bio-physical origin. Specific areas of their origin, usually from biological complex systems, will be discussed, with an emphasis put on their mathematical-physical algorithmic description and subsequent realization on PC. Students will eventually gain an interdisciplinary view of unconventional algorithms, complex systems and their dynamical behaviour. Graduates will acquire knowledge of modern computational methods for modelling and simulations of otherwise very complicated and complex systems (deterministic chaos, Thom’s catastrophe theory, fractal geometry, Swarm intelligence, quantum mechanics algorithms, cellular automata, “physarium machines”, “self-organized criticality”, …). The graduates will also become familiar with evolutionary computing techniques and current trends in the field of EVT. The Central dogma according to Darwin and EVT Mendel. Basic terms, computational complexity and theoretical limits of algorithms, P and NP problems. Selected stochastic algorithms: local search method, blind algorithm, hill climbing algorithm, and simulated annealing. Selected stochastic algorithms with evolutionary features: simulated annealing with elitist strategy, tabu search, particle swarm, scatter search, ant colony optimization and other algorithms.
Compulsory literature:
• Maurice Clerc. Particle Swarm Optimization, Wiley-ISTE, 2006.
• Marco Dorigo, Thomas Stutzle. Ant Colony Optimization, The MIT Press, 2004.
• Andries P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, Wiley, 2006.
Recommended literature:
• Kenneth Price, Rainer M. Storn, Jouni A. Lampinen. Differential Evolution: A Practical Approach to Global Optimization, Springer, 2005.
• Christine Solnon. Ant Colony Optimization and Constraint Programming, Wiley-ISTE, 2010.
• Yang Xiao, Fei Hu. Bio-inspired Computing and Communication Networks, CRC, 2010.
Way of continuous check of knowledge in the course of semester
E-learning
Other requirements
No other requirements.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
The aim of the subject is to introduce students to the area of unconventional algorithms and their bio-physical origin. Specific areas of their origin, usually from biological complex systems, will be discussed, with an emphasis put on their mathematical-physical algorithmic description and subsequent realization on PC. Students will eventually gain an interdisciplinary view of unconventional algorithms, complex systems and their dynamical behaviour. Graduates will acquire knowledge of modern computational methods for modelling and simulations of otherwise very complicated and complex systems (deterministic chaos, Thom’s catastrophe theory, fractal geometry, Swarm intelligence, quantum mechanics algorithms, cellular automata, “physarium machines”, “self-organized criticality”, …). The graduates will also become familiar with evolutionary computing techniques and current trends in the field of EVT. The Central dogma according to Darwin and EVT Mendel. Basic terms, computational complexity and theoretical limits of algorithms, P and NP problems. Selected stochastic algorithms: local search method, blind algorithm, hill climbing algorithm, and simulated annealing. Selected stochastic algorithms with evolutionary features: simulated annealing with elitist strategy, tabu search, particle swarm, scatter search, ant colony optimization and other algorithms.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks