Title:

Bio-Inspired Computers

Code:BIN
Ac.Year:2017/2018
Term:Summer
Curriculums:
ProgrammeFieldYearDuty
IT-MSC-2MBI1stCompulsory
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Elective
IT-MSC-2MIN-Compulsory-Elective - group I
IT-MSC-2MIS-Elective
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Compulsory-Elective - group N
IT-MSC-2MPV-Compulsory-Elective - group B
IT-MSC-2MSK-Elective
Language of Instruction:Czech
Private info:http://www.fit.vutbr.cz/study/courses/BIN/private/
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:2600818
 ExaminationTestsExercisesLaboratoriesOther
Points:52150825
Guarantor:Sekanina Lukáš, prof. Ing., Ph.D., DCSY
Lecturer:Bidlo Michal, Ing., Ph.D., DCSY
Sekanina Lukáš, prof. Ing., Ph.D., DCSY
Vašíček Zdeněk, doc. Ing., Ph.D., DCSY
Instructor:Bidlo Michal, Ing., Ph.D., DCSY
Husa Jakub, Ing., DCSY
Mrázek Vojtěch, Ing., DCSY
Vašíček Zdeněk, doc. Ing., Ph.D., DCSY
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Systems FIT BUT
Schedule:
DayLessonWeekRoomStartEndLect.Gr.St.G.EndG.
Tueexam - 1. oprava2018-05-29A11215:0016:501MIT
Tueexam - 1. oprava2018-05-29A11215:0016:502MIT
Friexam - řádná2018-05-11G20208:0009:501MIT
Friexam - řádná2018-05-11G20208:0009:502MIT
Friexam - 2. oprava2018-06-08L31408:0009:501MIT
Friexam - 2. oprava2018-06-08L31408:0009:502MIT
 
Learning objectives:
  To understand the principles of bio-inspired computational systems. To be able to use the bio-inspired techniques in the phase of design, implementation and runtime of a computational device.
Description:
  This course introduces computational models and computers which have appeared at the intersection of hardware and artificial intelligence in the recent years as an attempt to solve traditionally hard computational problems. The course surveys relevant theoretical models, reconfigurable architectures and computational intelligence techniques inspired at the levels of phylogeny, ontogeny and epigenesis. In particular, the following topics will be discussed: evolutionary design, evolvable hardware, cellular systems, embryonal and neural hardware, molecular computers and nanotechnology. Typical applications will illustrate the mentioned approaches.
Subject specific learning outcomes and competences:
  Students will be able to utilize evolutionary algorithms to design electronic circuits. They will be able to model, simulate and implement non-conventional, in particular bio-inspired, computational systems.
Generic learning outcomes and competences:
  Understanding the relation between computers (computing) and some natural processes.
Syllabus of lectures:
 
  1. Introduction, inspiration in biology, entropy and self-organization
  2. Limits of abstract and physical computing
  3. Evolutionary design
  4. Cartesian genetic programming
  5. Reconfigurable computing devices
  6. Evolutionary design of digital circuits
  7. Evolutionary circuit design, extreme environments
  8. Evolvable hardware, applications
  9. Computational development
  10. Neural hardware
  11. DNA computing
  12. Nanotechnology and molecular electronics
  13. Recent trends
Syllabus of computer exercises:
 
  1. Evolutionary design of combinational circuits
  2. Statistical evaluation of experiments with evolutionary design
  3. Virtual reconfigurable circuits
  4. Celulární automaty
Fundamental literature:
 
  • Sekanina L., Vašíček Z., Růžička R., Bidlo M., Jaroš J., Švenda P.: Evoluční hardware: Od automatického generování patentovatelných invencí k sebemodifikujícím se strojům. Academia Praha 2009, ISBN 978-80-200-1729-1 
  • Floreano, D., Mattiussi, C.: Bioinspired Artificial Intelligence: Theories, Methods, and Technologies. The MIT Press, Cambridge 2008, ISBN 978-0-262-06271-8
  • Trefzer M., Tyrrell A.M.: Evolvable Hardware - From Practice to Application. Berlin: Springer Verlag, 2015, ISBN 978-3-662-44615-7
  • Greenwood, G., Tyrrell, A.: Introduction to Evolvable Hardware. A Practical Guide for Designing Self-Adaptive Systems. IEEE Press Series on Computational Intelligence, 2006, ISBN 0-471-71977-3
  • Miller J.F.: Cartesian Genetic Programming, Springer Verlag 2011, ISBN 978-3-642-17309-7
Study literature:
 
  • Sekanina L., Vašíček Z., Růžička R., Bidlo M., Jaroš J., Švenda P.: Evoluční hardware: Od automatického generování patentovatelných invencí k sebemodifikujícím se strojům. Academia Praha 2009, ISBN 978-80-200-1729-1
  • Floreano, D., Mattiussi, C.: Bioinspired Artificial Intelligence: Theories, Methods, and Technologies. The MIT Press, Cambridge 2008, ISBN 978-0-262-06271-8
  • Trefzer M., Tyrrell A.M.: Evolvable Hardware - From Practice to Application. Berlin: Springer Verlag, 2015, ISBN 978-3-662-44615-7
  • Kvasnička, V., Pospíchal J., Tiňo P.: Evolučné algoritmy. Vydavatelství STU Bratislava, 2000, 215 s., ISBN 80-227-1377-5
  • Mařík et al.: Umělá inteligence IV, Academia, 2003, 480 s., ISBN 80-200-1044-0
Controlled instruction:
  Mid-term exam passing, realization and presentation/defense of project, computer lab assignments in due dates. In the case of a reported barrier to defend a project/solve a lab assignment, a student will be allowed to defend the project/solve the lab assignment  in an alternative date.
Progress assessment:
  Mid-term exam, project and its presentation, computer lab assignments. 
Exam prerequisites:
  None
 

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