Bi7447 Natural Computing

Faculty of Science
Spring 2011
Extent and Intensity
3/0/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
prof. Ing. Jiří Holčík, CSc. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course will provide students with an information about algorithms for task solving (optimization, classification, ...) inspired by living organisms. Basic and advanced principles of genetic algorithms as well as genetic programming and classification will be explained here. Further, the course will deal with problems of evolutionary and cellular systems, algorithms using strategy of an immune system and principles of molecular computing. Students will be able to apply the described algorithms for solving their tasks of given properties and parameters.
Syllabus
  • 1. Definitions and attributes of life, complexity, self-organisation, adaptivity, transmission of information. Artificial systems inspired by living organisms – phylogenetic, ontogenetic and epigenetic systems. 2. Phylogenetic systems – fundamentals of genetics. 3. Genetic algorithms – optimisation tasks, definition of GA, a simple GA, mathematical foundations – Schema Theorem, implicit parallelism, minimal deceptive problem, building block hypothesis, Hamming barrier 4. Basic genetic operators - reproduction, crossover, mutation, coding, objective function mapping, fitness scaling 5. GA with real value parameters, stochastic GA 6. Advanced genetic operators and techniques – diploidy, dominance, masking, inversion 7. Reordering operators, sexual determination and differenciation, niche. 8. Genetic programming. Genetic classification. 9. Evolutionary systems – evolutionary strategies 10. Evolutionary systems – evolutionary programming 11. Ontogenetic systems – organism development, cellular automata, ant algorithms. 12. Epigenetic systems – principles of biological immunity – elements of immune system, affinity and its development, memory of the IS, tolerance, intracellular pathogens. 13. Artificial immune system 14. Molecular computing, DNA computing
Literature
  • KVASNIČKA V., POSPÍCHAL J., TIŇO P. Evolučné algoritmy. Bratislava, STU 2000.
  • Evolutionary Computation. The Fosil Record. FOGEL D.B. (ed.) New York, IEEE Press 1998.
  • HOLČÍK J. STRASZECKA E. Bionika. [VŠ skripta], Brno, ÚBMI FEI VUT v Brně 1999.
  • PAUN G., ROZENBERG G., SALOMAA A. DNA Computing. New Computing Paradigma. Berlin, Springer Verlag 1998
  • GOLDBERG D.E. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Addison-Wesley Publ. Comp 1989
  • HOLLAND J.H. Adaptation in Natural and Artificial Systems. Cambridge, MIT Press 1993.
  • Artificial Immune Systems and Their Application. D. DASGUPTA (ed.), Berlin, Springer Verlag 1998.
Assessment methods
oral examination
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2011 - only for the accreditation, Spring 2009, Spring 2010.
  • Enrolment Statistics (recent)
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