Semester: 8
ECTS: 6
Lectures: 30
Practice sessions: 30
Independent work: 120
Module Code: 24-000-0150
Semester: 8
ECTS: 6
Lectures: 30
Practice sessions: 30
Independent work: 120
Module Code: 24-000-0150

Module title:


Nature-inspired optimization algorithms

Lecturers and associates:



Module overview:


This module introduces students to the optimization domain, P and NP complexity, and provides insight into algorithms inspired by evolution (genes, evolutionary strategies), animal behavior (ant colony, swarms, bees), and other biological or evolutionary systems.

This module is intended for students who want to connect real-world problems (routing problems, scheduling problems, etc.) with evolutionary and soft computation solutions.

This module encourages students to apply the theory, especially NP-hard or complete problems, taught in the rest of the program in a practical situation. Moreover, this module prepares students for jobs where optimization is the core of a business model, and where a student should be able to recognize a problem type and the most appropriate algorithmic solution. Skills learned in this module will contribute significantly to students’ development as professionals in respecting fields.

In this module students will learn:

About problem complexity

About different approaches to solving optimization problems

About different nature-inspired computational algorithms

How to create a software solution that uses nature-inspired computation algorithms.

How to analyse optimization problem solutions and find the correct hyperparameters

How to use optimization algorithms to find optimal parameters of specific machine learning model


Literature:


Required readings:
1. Affenzeller, M. et al (2009) Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. London: Chapman and Hall.
2. Dorigo, M. and Stutzle, T. (2004) Ant Colony Optimization. Cambridge: A Bradford Book.

Supplementary readings:
1. 1. Price, K., Storn, R.M. and Lampinen, J.A. (2014) Differential Evolution: A Practical Approach to Global Optimization. New York City: Springer.
2. 2. Deb, K. (2009) Multi-Objective Optimization Using Evolutionary Algorithms. Hoboken: Wiley.