Biologically inspired computing

1.

Subject title

Biologically inspired computing

Биолошки инспирирано пресметување

2.

Code

F23L3S078

3.

Study program

Софтверско инженерство и информациски системи, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Информатичка едукација, Software engineering and information systems, Примена на информациски технологии, Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Software engineering and information systems, Стручни студии за програмирање, Стручни студии за програмирање,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Прв циклус

6.

Academic year / semester

4 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

доц. д-р Илинка Иваноска

9.

Prerequisites for enrollment

Алгоритми и податочни структури или Примена на алгоритми и податочни структури

10.

Subject goals and competencies:

The goal of this course is to familiarize students with algorithms that are inspired by phenomena that occur in nature and apply them to solve optimization, design, and learning problems. The focus will be on the abstraction of the algorithms from the perceived phenomena, analysis of their results as well as their comparison. During the course, attention will be paid to specific applications of the mentioned algorithms. After completing the course, students are expected to acquire:

11.

Subject content:

Lectures: 1. Introduction to Biologically Inspired Computing; Search and optimization; 2. Local search techniques; 3. Genetic algorithms 1; 4. Genetic algorithms 2; 5. Genetic programming; 6. Herd intelligence; Ant Colony Optimization; 7. Particle swarm optimization; Artificial colony of bees; 8. Artificial immune systems; 9. Neural networks; 10. Self-organizing neural networks; 11. Satisfaction of restrictions; 12. Other biologically inspired heuristics; Exercises: 1. Introduction to Biologically Inspired Computing; Search and optimization; 2. Local search techniques; 3. Genetic algorithms 1; 4. Genetic algorithms 2; 5. Genetic programming; 6. Herd intelligence; Ant Colony Optimization; 7. Particle swarm optimization; Artificial colony of bees; 8. Artificial immune systems; 9. Neural networks; 10. Self-organizing neural networks; 11. Satisfaction of restrictions; 12. Other biologically inspired heuristics;

12.

Learning methods:

Предавања со користење на презентации, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа.

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

45 hours

16.

Other forms of activities

16.1.

Project tasks

15 hours

16.2.

Independent tasks

15 hours

16.3.

Homework

75 hours

17.

Grading method

17.1.

Tests

10 points

17.2.

Seminar work / project (presentation: written and oral)

15 points

17.3.

Activities and learning

10 points

17.4.

Final exam

70 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

Реализирани актибвности 15.2 и 16.1

20.

Language of instruction

Македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature