Title:

# Evolutionary Computation

Code:EVD
Ac.Year:2018/2019
Term:Summer
Curriculums:
ProgrammeFieldYearDuty
CSE-PHD-4DVI4-Elective
Language of Instruction:Czech
Private info:http://www.fit.vutbr.cz/study/courses/EVD/private/
Completion:examination (verbal)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:390000
ExamsTestsExercisesLaboratoriesOther
Points:5100049
Guarantor:Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Lecturer:Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Schwarz Josef, doc. Ing., CSc. (DCSY)
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Systems FIT BUT

Learning objectives:
To inform the students about up to date algorithms for solution of complex, NP complete problems.
Description:
Evolutionary computation in the context of artificial intelligence and optimization problems with NP complexity. Paradigm of genetic algorithms, evolutionary strategy, genetic programming and another evolutionary heuristics. Theory and practice of standard evolutionary computation. Advanced evolutionary algorithms based on graphic probabilistic models (EDA - estimation of distribution algorithms). Parallel evolutionary algorithms. A survey of representative applications of evolutionary algorithms in multi-objection optimization problems, artificial intelligence, knowledge based systems and digital circuit design. Techniques of rapid prototyping of evolutionary algorithms.
Learning outcomes and competences:
Skills and approaches in solution of hard optimization problems.
Syllabus of lectures:

• Evolutionary algorithms, theoretical foundation, basic distribution.
• Genetic algorithms (GA), schemata theory.
• Repesentative combinatorial optimization problems.
• Evolution strategies.
• Genetic programming.
• Advanced estimation distribution algorithms (EDA).
• Variants of EDA algorithms, UMDA, BMDA and BOA.
• Simulated annealing.
• Methods for multicriterial and multimodal problems. Selection and population replacement.
• Techniques for fast prototyping. Structure of development systems and GA library.
• New evolutionary paradigm: immune systems,  differential evolution, SOMA.
Syllabus - others, projects and individual work of students:

• Defence of a project, software project based on a variant of evolutionary algorithm
Fundamental literature:

• Back, J: Evolutionary algorithms, theory and practice, New York, 1996.
• Goldberg, D., E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Boston, MA: Kluwer Academic Publishers, 2002. ISBN: 1402070985.
• Kvasnička V., Pospíchal J., Tiňo P.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 80-227-1377-5.
Study literature:

• Fogel D., B.: Evolutionary computation: Toward a new philosophy of machine intelligence. IEEE Press, New York, 2000, ISBN 0-7803-5379-X.
Controlled instruction:
Project defence, software project based on a variant of evolutionary algorithms or the  presentation of the assigned task.