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ECTS Course Overview



Biologically Inspired Algorithms

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code460-4086/02
Number of ECTS Credits Allocated4 ECTS credits
Type of Course Unit *Optional
Level of Course Unit *Second Cycle
Year of Study *
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech, English
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
ZEL01prof. Ing. Ivan Zelinka, Ph.D.
SKA206Ing. Lenka Skanderová, Ph.D.
Summary
The course will discuss a wider range of evolutionary computation. They
mentioned as historically classic techniques and modern algorithms. There will also be discussed at the introductory level, cellular automata, artificial life, neural networks, evolutionary hardware, DNA computing, etc. Great emphasis will be placed on the practical side of things - the ability to most discussed methods applied to practical examples. Students should have the comprehensive knowledge of the course of the above areas, including the possibility its use. The course includes laboratory exercises in which students will practice
how to program the selected algorithms and their application to solving practical
problems.
Learning Outcomes of the Course Unit
The goal is to introduce the students with modern methods of calculation derived from evolutionary and biological processes (evolutionary algorithms, cellular automata etc.). Student will gain an overview of modern computer-based procedures principles of observation of biological processes and dynamics. Upon successful completion of graduate course will be able to apply the methods discussed in the course to real problems.
Course Contents
Lectures:

1.Evolutionary algorithms 1. The current state of the field softcomputing, fuzzy logic, neural
networks, evolutionary computing (EVT). Classification of evolutionary computation,
historical facts, current trends in EVT. The central dogma, according to Darwin, and EVT
Mendel.

2. Evolutionary Algorithms 2. No Free Lunch Theorem. Computational complexity and physical limitations
algorithms. Multipurpose optimization and Pareto set.

3. Evolutionary algorithms 3. Restrictions placed on the utility function and individual parameters.
Penalties and its impact on the geometry of the objective function. Working with real, integer and
discrete values ​​of individual parameters. Genetic algorithms. GA terminology. Principle
activities, Hybrid GA, messy GA, parallel GA, migration and diffusion models.

4. Evolutionary algorithms 4. Evolutionary Strategy. No-man (1 +1)-EC.Multi-EC (μ
+ Λ)-ES and (μ, λ)-ES. Multi-EC (μ + λ)-ES and (μ, λ)-ES. Adaptive EC.Particle swarm
(Particle Swarm). Search suspended (Scatter Search). Ant colony optimization (ant
Colony Optimization).

5. Evoluční algorithms 5. SOMA: Self-Organizing Migrating Algorithm principle of operation
and strategies used by the algorithm: ATO, ATR ATAA and ATA. Differential evolution principle
activities 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.

6. Evolutionary algorithms 6. Techniques of Genetic Programming: Genetic Programming,
grammatical evolution. Alternatives: analytical programming, Probabilistic Incremental
Program Evolution - PIPE, Gene Expression Programming, Programming Multiexpression
and more.

7. Evolutionary Hardware (EH). Inspiration in biology. Computing devices.Reconfigurable
equipment. Evolutionary design and digital circuits. EH and cellular automata. Polymorphous
electronics.

8. Cellular Automata (BA) and complex systems. Introduction, Formalism BA
Dynamics and classification according to Wolfram's cellular automata, Conway's Game of Life,
using BA modeling.

9. Artificial life. Basic definitions and existing systems and models. Tierra, biomorf, Sbeat,
Sbart, Eden, Galapagos ... Self-reproducing automata according to Turing and von
Neumann. Langton's loop, computer viruses and artificial life. Artificial Life and edge
chaos (according to Kaufmann)

10. Neural Networks (ANN). History and basic principles of NS. The training set and its use
NS. The basic types of networks and their applications to different types of problems.

11. Fractal geometry. History, definition of fractal, basic types of algorithms that generate
fractals. Fractal dimension, interpolation and compression. Developmental systems and artificial life. L systems,
turtle graphics, parametric L-systems, L-systems from the perspective of fractal geometry.

12. Immunological systems (IS). The principle of the IS, the IS limits, algorithms implementing IS imunotronics.

13. Swarm Intelligence (SI). Basic concepts and definitions, representative algorithms SI -
Particle Swarm, scatter search, ant colony optimization, swarm robotic, artificial evolution
complex systems.

14. DNA computing. DNA computing as part of bioinformatics, DNA and binary
representation according to Adlemann. Watson Crickův machine. Mathematical modeling operations on DNA.


Laboratories (for PC classrooms):
The seminar will focus on the practical application of the discussed techniques and solutions of selected problem examples.
- Creation of a single basic framework for bio-inspired algorithms on the principles of GUI, 1 week
- Creation of a module for generating population and fitness function, 1 week
- Creation of a module selection techniques for parents (suitable candidates) to create offspring (better solution), 1 week
- Creation of a module for crossover, 1 week
- Creation of a modules of evolutionary algorithms, 4 weeks
- Creation of a modules of symbolic regression, 4 weeks
- Creation of a module with a simple cellular automaton, 1 week
Recommended or Required Reading
Required Reading:
Back, T., Fogel, B., Michalewicz, Z.: Handbook of Evolutionary Computation, Institute of Physics, London
Davis L. 1996, Handbook of Genetic Algorithms, International Thomson Computer Press, ISBN 1850328250
Koza J.R. 1998, Genetic Programming, MIT Press, ISBN 0-262-11189-6
Price,K.,Storn,R.,etal.:DifferentialEvolution-APracticalApproachtoGlobalOptimization. Springer, Heidelberg

1. Zelinka I., Oplatková Z., Šeda M., Ošmera P., Včelař F., Evoluční výpočetní
techniky, principy a aplikace, BEN, 2008, Praha
2. Kvasnička V., Pospíchal J., Tiňo P., Evolučné algoritmy, STU Bralislava, ISBN
80-227-1377-5, 2000
3. Zelinka I., Včelař F., Čandík M., Fraktální geometrie – principy a aplikace, BEN, 2006, 160 p., ISBN 80-7300-191-8
4. Back, T., Fogel, B., Michalewicz, Z.: Handbook of Evolutionary Computation, Institute of Physics, London
5. Davis L. 1996, Handbook of Genetic Algorithms, International Thomson Computer Press, ISBN 1850328250
6. Koza J.R. 1998, Genetic Programming, MIT Press, ISBN 0-262-11189-6
7. Price,K.,Storn,R.,etal.:DifferentialEvolution-APracticalApproachtoGlobalOptimization. Springer, Heidelberg
Recommended Reading:
Ilachinsky A., Cellular Automata: A Discrete Universe, World Scientific Publishing,
ISBN 978-9812381835, 2001
Hilborn R.C.1994, Chaos and Nonlinear Dynamics, Oxford University Press, ISBN
0-19-508816-8, 1994
Gheorghe Paun (Author), Grzegorz Rozenberg (Author), Arto Salomaa, DNA
Computing: New Computing Paradigms, Springer, ISBN 978-3540641964
1. Mařík V. Štěpánková O., Lažanský J., Umělá inteligence IV, Academia, Praha,
ISBN 80-200-1044-0, 2004
2. Mařík V. Štěpánková O., Lažanský J., Umělá inteligence III, Academia, Praha,
ISBN 80-200-0472-6, 2001
3. Ilachinsky A., Cellular Automata: A Discrete Universe, World Scientific Publishing,
ISBN 978-9812381835, 2001
Hilborn R.C.1994, Chaos and Nonlinear Dynamics, Oxford University Press, ISBN
0-19-508816-8, 1994
Gheorghe Paun (Author), Grzegorz Rozenberg (Author), Arto Salomaa, DNA
Computing: New Computing Paradigms, Springer, ISBN 978-3540641964
Planned learning activities and teaching methods
Lectures, Tutorials
Assesment methods and criteria
Tasks are not Defined