460-4086/02 – Biologically Inspired Algorithms (BIA)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantorprof. Ing. Ivan Zelinka, Ph.D.Subject version guarantorprof. Ing. Ivan Zelinka, Ph.D.
Study levelundergraduate or graduateRequirementOptional
Year1Semesterwinter
Study languageEnglish
Year of introduction2015/2016Year of cancellation
Intended for the facultiesUSP, FEI, HGFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
SKA206 Ing. Lenka Skanderová, Ph.D.
ZEL01 prof. Ing. Ivan Zelinka, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Part-time Credit and Examination 10+0

Subject aims expressed by acquired skills and competences

The aim of the course is to acquaint its students with modern computational methods derived from evolutionary and biological processes (evolutionary algorithms, cellular automata, etc.). The graduate will learn to program and use popular algorithms in the field of evolution and swarm intelligence and apply them to real problems. He will also gain an overview of modern computational procedures based on principles observed from biological processes and dynamics. Upon successful completion of the course, the graduate will be able to apply the methods discussed in the course to real problems of practice.

Teaching methods

Lectures
Tutorials

Summary

The course will discuss a wider range of evolutionary computing techniques. Both historically classical techniques and modern algorithms will be mentioned. Evolutionary algorithms and swarm intelligence such as simulated annealing, genetic algorithm, differential evolution, particle swarm, SOMA and others will be discussed. In the second part, the student gets acquainted with symbolic regression and its use in the synthesis of algorithms, classifiers or control programs. After completing the course, the student should have comprehensive knowledge of the above areas, including the possibility of their use. Part of the course is laboratory exercises, in which students will practice both programmings of selected algorithms and their application to solving practical problems.

Compulsory literature:

1. Back, T., Fogel, B., Michalewicz, Z.: Handbook of Evolutionary Computation, Institute of Physics, London 2. Davis L. 1996, Handbook of Genetic Algorithms, International Thomson Computer Press, ISBN 1850328250 3. Koza J.R. 1998, Genetic Programming, MIT Press, ISBN 0-262-11189-6 4. Price,K.,Storn,R.,etal.:DifferentialEvolution-APracticalApproachtoGlobalOptimization. Springer, Heidelberg

Recommended literature:

5. Ilachinsky A., Cellular Automata: A Discrete Universe, World Scientific Publishing, ISBN 978-9812381835, 2001 6. Hilborn R.C.1994, Chaos and Nonlinear Dynamics, Oxford University Press, ISBN 0-19-508816-8, 1994 7. Gheorghe Paun (Author), Grzegorz Rozenberg (Author), Arto Salomaa, DNA Computing: New Computing Paradigms, Springer, ISBN 978-3540641964

Way of continuous check of knowledge in the course of semester

The examination is based on the elaboration of the protocols of the subject, by which the student demonstrates not only the understanding of the lecture information but also the ability to implement them in the given programming environment. To obtain a credit, you must hand over all the required protocols and have at least 80% of physical attendance at the laboratories. Credit is a vital condition for admission to the exam. The exam is oral.

E-learning

Other requirements

It is required the ability to create programs in arbitrary programming language and apply lecture knowledge into algorithms. Additional requirements are not defined.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: 1.Current state in the field of soft computing, fuzzy logic, neural networks, evolutionary computing (EVT). Classification of evolutionary computing techniques, historical facts, current trends in the field of EVT. The central dogma of EVT according to Darwin and Mendel. 2. No Free Lunch Theorem. Computational complexity and physical limits of algorithms. 3. Limitations placed on the cost function and parameters of the individual. Penalty and its impact on the geometry of the cost function. Work with real, integer and discrete values of individual parameters. 4. Test benchmark function. 5. Multi and many-objective optimization and Pareto set. Critical situations in the algorithm run and their solution. 6. Blind search and climbing algorithm. 7. Genetic algorithms. GA terminology. Principle of operation, Hybrid GA, messy GA, parallel GA, migration and diffusion model. 8. Evolutionary strategies. Two-member ES: (1 + 1) -ES. Multipart ES: (μ + λ) -ES and (μ, λ) -ES. Multipart ES: (μ + λ) -ES and (μ, λ) -ES. Adaptive ES. 9. Particle swarm. Scatter Search. AntColony Optimization. 10. SOMA: Self-organizing Migration Algorithm, the principle of operation and used algorithm strategies: ATO, ATR, ATA and ATAA. 11. Differential evolution, principle of operation and used versions: DE / best / 1 / exp, DE / rand / 1 / exp, DE / rand-to-best / 1 / exp, DE / best / 2 / exp, DE / rand / 2 / exp, DE / best / 1 / bin, DE / rand / 1 / bin, DE / rand-to-best / 1 / bin, DE / best / 2 / bin, DE / rand / 2 / bin. SOMA, DE and permutation test problems. 12. Swarm intelligence (SI). Basic terms and definitions, representative SI algorithms - particle swarm, scatter search, ant colony optimization, swarm robotic, artificial evolution of complex systems. 13. Techniques of symbolic regression: genetic programming, grammatical evolution. Alternatives: Analytical Programming, Probabilistic Incremental Program Evolution - PIPE, Gene Expression Programming, Multiexpression Programming and more. 14. Case studies Laboratories (for PC classrooms): The seminar will focus on the practical application of the discussed techniques and solutions of selected problem examples. • Implementation of selected benchmark functions (optimization problems) • Implementation of the Blind Algorithm and the Climbing Algorithm • Implementation of the Genetic Algorithm and its application to the business traveller problem (TSP) • Implementation of Differential Evolution • Implementation of Evolutionary Strategies • Implementation of the Particle Swarm Optimization algorithm using inertia • Implementation of a Self-Organizing Migration Algorithm (SOMA), comparison of the behavior of SOMA and PSO algorithms • Implementation of Firefly algorithm • Implementation of the Ant Colony Optimization (ACO) algorithm and its application to the business traveller problem. Comparison of ACO and GA in terms of convergence speed and accuracy of the resulting solution • Teaching-learning based algorithm • Multiobjective optimization. Implementation of NSGA II algorithm (Non-dominated Sorting Genetic Algorithm)

Conditions for subject completion

Part-time form (validity from: 2015/2016 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 45  25
        Examination Examination 55  6
Mandatory attendence parzicipation:

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2021/2022 (N0688A140015) Industry 4.0 P English Ostrava 2 Choice-compulsory type B study plan
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2021/2022 (N0541A170008) Computational and Applied Mathematics (S01) Applied Mathematics P English Ostrava Optional study plan
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2021/2022 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 2 Choice-compulsory study plan
2021/2022 (N2647) Information and Communication Technology (2612T059) Mobile Technology P English Ostrava 1 Optional study plan
2021/2022 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava Choice-compulsory study plan
2020/2021 (N0688A140015) Industry 4.0 P English Ostrava 2 Choice-compulsory type B study plan
2020/2021 (N0714A150002) Control and Information Systems P English Ostrava 2 Optional study plan
2020/2021 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 2 Choice-compulsory study plan
2020/2021 (N0612A140005) Information and Communication Security P English Ostrava 1 Optional study plan
2020/2021 (N2647) Information and Communication Technology (1801T064) Information and Communication Security P English Ostrava 1 Optional study plan
2020/2021 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava Choice-compulsory study plan
2020/2021 (N2647) Information and Communication Technology (2612T059) Mobile Technology P English Ostrava 1 Optional study plan
2020/2021 (N0541A170008) Computational and Applied Mathematics (S02) Computational Methods and HPC P English Ostrava Optional study plan
2020/2021 (N0541A170008) Computational and Applied Mathematics (S01) Applied Mathematics P English Ostrava Optional study plan
2019/2020 (N2647) Information and Communication Technology (1801T064) Information and Communication Security P English Ostrava 1 Optional study plan
2019/2020 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava Choice-compulsory study plan
2019/2020 (N2647) Information and Communication Technology (2612T059) Mobile Technology P English Ostrava 1 Optional study plan
2019/2020 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava Choice-compulsory study plan
2019/2020 (N2647) Information and Communication Technology (2612T059) Mobile Technology K English Ostrava 1 Optional study plan
2019/2020 (N0714A150002) Control and Information Systems P English Ostrava 2 Optional study plan
2019/2020 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 2 Choice-compulsory study plan
2019/2020 (N0541A170008) Computational and Applied Mathematics (S01) Applied Mathematics P English Ostrava Compulsory study plan
2019/2020 (N0541A170008) Computational and Applied Mathematics (S02) Computational Methods and HPC P English Ostrava Optional study plan
2019/2020 (N0612A140005) Information and Communication Security P English Ostrava 1 Optional study plan
2018/2019 (N2647) Information and Communication Technology (1801T064) Information and Communication Security P English Ostrava 1 Optional study plan
2018/2019 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava Choice-compulsory study plan
2018/2019 (N2647) Information and Communication Technology (2612T059) Mobile Technology P English Ostrava 1 Optional study plan
2018/2019 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava Choice-compulsory study plan
2018/2019 (N2647) Information and Communication Technology (2612T059) Mobile Technology K English Ostrava 1 Optional study plan
2018/2019 (N2658) Computational Sciences (2612T078) Computational Sciences P English Ostrava 1 Choice-compulsory study plan
2018/2019 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 2 Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (1801T064) Information and Communication Security P English Ostrava 1 Optional study plan
2017/2018 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (2612T059) Mobile Technology P English Ostrava 1 Optional study plan
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2017/2018 (N2658) Computational Sciences (2612T078) Computational Sciences P English Ostrava 1 Choice-compulsory study plan
2016/2017 (N2658) Computational Sciences (2612T078) Computational Sciences P English Ostrava 1 Choice-compulsory study plan
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2016/2017 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava Choice-compulsory study plan
2016/2017 (N2647) Information and Communication Technology (2612T059) Mobile Technology P English Ostrava 1 Optional study plan
2016/2017 (N2647) Information and Communication Technology (2612T059) Mobile Technology K English Ostrava 1 Optional study plan
2016/2017 (N2647) Information and Communication Technology (1801T064) Information and Communication Security P English Ostrava 1 Optional study plan
2015/2016 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava Choice-compulsory study plan
2015/2016 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava Choice-compulsory study plan
2015/2016 (N2647) Information and Communication Technology (2612T059) Mobile Technology P English Ostrava 1 Optional study plan
2015/2016 (N2647) Information and Communication Technology (2612T059) Mobile Technology K English Ostrava 1 Optional study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner
V - ECTS - mgr. 2021/2022 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2020/2021 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2019/2020 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2018/2019 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2017/2018 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2016/2017 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2015/2016 Full-time English Optional 401 - Study Office stu. block