Evolutionary Programming

Major: Computer Sciences
Code of subject: 7.122.04.E.74
Credits: 5.00
Department: Artificial Intelligence Systems
Lecturer: Shvorob I.
Semester: 2 семестр
Mode of study: денна
Learning outcomes: Be able to formulate and improve an important research task, to solve it to collect the necessary information and to formulate conclusions that can be defended in a scientific context. Use professional knowledge and practical skills to optimize the design of information systems of any complexity, to solve specific problems of designing intelligent information systems for the management of objects of different physical nature. Be able to analyze and evaluate the range of tasks that contribute to the further development of effective use of information resources of decision-making systems.
Required prior and related subjects: Previous academic disciplines Data mining Methods and means of data integration Decision theory Related and subsequent disciplines Project implementation process management Data visualization Machine learning
Summary of the subject: The purpose of teaching the discipline "Evolutionary Programming" is to study and practice the methods and algorithms of evolutionary programming, preparing students for the effective use of modern evolutionary methods for creation of automated systems in further professional activity; acquisition of practical skills in working with software tools for building intelligent models based on methods of evolutionary optimization.
Assessment methods and criteria: 40 points - laboratory work 60 points - exam
Recommended books: 1. Encyclopediaofartificialintelligence / Eds.: J. R. Dopico, J. D. delaCalle,A. P. Sierra. – NewYork :InformationScienceReference, 2009. – Vol. 1-3. – 1677 p. 2. Gen M. Geneticalgorithmsandengineeringdesign / M. Gen, R. Cheng. –NewJersey :JohnWiley&Sons, 1997. – 352 p. 3. Haupt R. Practicalgeneticalgorithms / R. Haupt, S. Haupt. – NewJersey :JohnWiley&Sons,2004. – 261 p. 4. Емельянов В. В. Теория и практика эволюционного моделирования /В. В. Емельянов, В. В. Курейчик, В. М. Курейчик. – М. :Физматлит, 2003. – 432 с. 5. Курейчик В. М. Генетические алгоритмы: монография /В. М. Курейчик. – Таганрог : ТРТУ, 1998. – 242 с.

Evolutionary Programming (курсова робота)

Major: Computer Sciences
Code of subject: 7.122.04.E.75
Credits: 2.00
Department: Artificial Intelligence Systems
Lecturer: Shvorob I.
Semester: 2 семестр
Mode of study: денна
Learning outcomes: Be able to formulate and improve an important research task, to solve it to collect the necessary information and to formulate conclusions that can be defended in a scientific context. Use professional knowledge and practical skills to optimize the design of information systems of any complexity, to solve specific problems of designing intelligent information systems for the management of objects of different physical nature. Be able to analyze and evaluate the range of tasks that contribute to the further development of effective use of information resources of decision-making systems.
Required prior and related subjects: Previous academic disciplines Data mining Methods and means of data integration Decision theory Related and subsequent disciplines Project implementation process management Data visualization Machine learning
Summary of the subject: The purpose of teaching the discipline "Evolutionary Programming" is to study and practice the methods and algorithms of evolutionary programming, preparing students for the effective use of modern evolutionary methods for creation of automated systems in further professional activity; acquisition of practical skills in working with software tools for building intelligent models based on methods of evolutionary optimization.
Assessment methods and criteria: course work – 100
Recommended books: 1. Encyclopediaofartificialintelligence / Eds.: J. R. Dopico, J. D. delaCalle,A. P. Sierra. – NewYork :InformationScienceReference, 2009. – Vol. 1-3. – 1677 p. 2. Gen M. Geneticalgorithmsandengineeringdesign / M. Gen, R. Cheng. –NewJersey :JohnWiley&Sons, 1997. – 352 p. 3. Haupt R. Practicalgeneticalgorithms / R. Haupt, S. Haupt. – NewJersey :JohnWiley&Sons,2004. – 261 p. 4. Емельянов В. В. Теория и практика эволюционного моделирования /В. В. Емельянов, В. В. Курейчик, В. М. Курейчик. – М. :Физматлит, 2003. – 432 с. 5. Курейчик В. М. Генетические алгоритмы: монография /В. М. Курейчик. – Таганрог : ТРТУ, 1998. – 242 с.