Subject

Biologically inspired computing

Course Title

Biologically inspired computing
Biologically inspired computing

Code

F23L3S078

Study Programme

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Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

8 / Summer

Number of ECTS credits

Teacher

Ilinka Ivanoska

Prerequisites for enrolling in the course

Algorithms and Data Structures or Applied Algorithms and Data Structures

10.

Objectives of the course programme (competences)

The goal of this course is to introduce students to algorithms inspired by phenomena that occur in nature and to apply them to solve problems in optimization, design, and learning. The focus will be on the abstraction of algorithms from observed phenomena, the analysis of their results, and their comparison. Throughout the course, attention will be paid to specific applications of the aforementioned algorithms. Upon completion of the course, students are expected to have acquired:
- Knowledge of the natural phenomena that inspire the discussed algorithms
Understanding the strengths and weaknesses of algorithms
Ability to identify the suitability of algorithms and their application to problems in optimization, design, and learning.

11.

Course content

Lectures:
1. Introduction to biologically inspired computing; Search and optimization;
2. Techniques for local search;
3. Genetic Algorithms 1;
4. Genetic Algorithms 2;
5. Genetic programming;
6. Swarm intelligence; Ant colony optimization;
7. Particle swarm optimization; Artificial bee colony;
8. Artificial immune systems;
9. Neural networks;
10. Self-organizing neural networks;
11. Satisfaction of restrictions;
12. Other biologically inspired heuristics;

Practical Classes:
1. Introduction to biologically inspired computing; Search and optimization;
2. Techniques for local search;
3. Genetic Algorithms 1;
4. Genetic Algorithms 2;
5. Genetic programming;
6. Swarm intelligence; Ant colony optimization;
7. Particle swarm optimization; Artificial bee colony;
8. Artificial immune systems;
9. Neural networks;
10. Self-organizing neural networks;
11. Satisfaction of restrictions;
12. Other biologically inspired heuristics;

12.

Learning methods

Lectures using presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defense of a project assignment and a seminar paper.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

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

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	10 points
17.2.	Seminar paper / 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.

Requirement for obtaining a signature and taking the final exam

Activities 15.2 and 16.1 have been completed.

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. L. N. de Castro \| Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications \| CRC Press \| 2006 2. D. Floreano and C. Mattiussi \| Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies \| MIT Press \| 2008 3. D. Simon \| Evolutionary Optimization Algorithms \| Wiley \| 2013
22.2.	Additional literature No. Author Title Publisher Year