9600-0009/01 – Unconvential Algorithms and Computations (NAV)

Gurantor departmentIT4InnovationsCredits10
Subject guarantorprof. Ing. Ivan Zelinka, Ph.D.Subject version guarantorprof. Ing. Ivan Zelinka, Ph.D.
Study levelpostgraduate
Study languageCzech
Year of introduction2015/2016Year of cancellation
Intended for the facultiesUSPIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
ZEL01 prof. Ing. Ivan Zelinka, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 2+0
Combined Examination 10+0

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

Další požadavky na studenta

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

Full-time form (validity from: 2015/2016 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history
Combined form (validity from: 2015/2016 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2018/2019 (P2658) Computational Sciences (2612V078) Computational Sciences P Czech Ostrava Choice-compulsory study plan
2018/2019 (P2658) Computational Sciences (2612V078) Computational Sciences K Czech Ostrava Choice-compulsory study plan
2017/2018 (P2658) Computational Sciences (2612V078) Computational Sciences P Czech Ostrava Choice-compulsory study plan
2017/2018 (P2658) Computational Sciences (2612V078) Computational Sciences K Czech Ostrava Choice-compulsory study plan
2016/2017 (P2658) Computational Sciences (2612V078) Computational Sciences P Czech Ostrava Choice-compulsory study plan
2016/2017 (P2658) Computational Sciences (2612V078) Computational Sciences K Czech Ostrava Choice-compulsory study plan
2015/2016 (P2658) Computational Sciences (2612V078) Computational Sciences P Czech Ostrava Choice-compulsory study plan
2015/2016 (P2658) Computational Sciences (2612V078) Computational Sciences K Czech Ostrava Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner