Nature-Inspired Algorithms WM-MA-AIN

The course aims to provide knowledge about the structure and operation of various heuristic optimization methods, including those inspired by natural mechanisms, and practical skills in using these methods. The methods find suboptimal solutions to computationally complex problems, the so-called NP-hard problems. The methods are based on a single solution (local search, simulated annealing, tabu) and a population-based approach (evolutionary algorithms, swarm optimization: particle swarms, and ant algorithms). Theory and pseudocodes for each method are provided and discussed. Multi-criteria optimization methods are also briefly discussed. Students are expected to be familiar with at least one computer programming language. As part of laboratory classes, students develop computer programs illustrating subsequent issues presented during the lecture and test them and their effectiveness on classic benchmark sets.

(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się

mathematics

(in Polish) E-Learning

Term 2023/24_Z:

(in Polish) E-Learning

Term 2022/23_Z:

(in Polish) E-Learning (pełny kurs)

(in Polish) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

(in Polish) Opis nakładu pracy studenta w ECTS

(in Polish) szacunkowy nakład pracy studenta - wykład: uczestnictwo w zajęciach 30 godz przygotowanie do zajęć 27 godz przygotowanie do weryfikacji 25 godz konsultacje z prowadzącym 3 godz Razem: 85 godz (3 ECTS) szacunkowy nakład pracy studenta - laboratorium: uczestnictwo w zajęciach 30 godz przygotowanie do zajęć 40 godz przygotowanie do weryfikacji 12 godz konsultacje z prowadzącym 3 godz Razem: 85 godz (3 ECTS)

Subject level

intermediate

Learning outcome code/codes

Lectures: MA2_W04, MA2_W05, MA2_W06, MA2_W07, MA2_W16 Lab: MA2_U13, MA2_U14, MA2_U15, MA2_U21, MA2_U24; MA2_K02, MA2_K08

Type of subject

optional with unlimited choices

Preliminary Requirements

requested basic knowledge concerning probabilistic theory, and computer programming skills.

Course coordinators

Term 2023/24_Z:

Krzysztof Trojanowski

Term 2022/23_Z:

Franciszek Seredyński

Learning outcomes

LECTURES
Student
W1 - knows techniques of optimization based on mechanisms of Nature and their applications in selected areas (MA2_W04, MA2_W05, MA2_W06, MA2_W07, MA2_W16)

LABS
Student
U1 - applies advanced tools and computer science methods based on techniques inspired by Nature in a selected area (MA2_U13, MA2_U14, MA2_U15, MA2_U21, MA2_U24)
K1 - is ready for systematic work in a project (MA2_K02, MA2_K08)

Assessment criteria

For all learning outcomes, the following assessment criteria are adopted for all forms of verification:
grade 5: fully achieved (no obvious shortcomings),
grade 4.5: achieved almost fully and criteria for awarding a higher grade are not met,
grade 4: largely achieved and the criteria for a higher grade are not met,
grade 3.5: largely achieved - with a clear majority of positives - and the criteria for granting a higher grade are not met,
grade 3: achieved for most of the cases covered by the verification and criteria for a higher grade are not met,
grade 2: not achieved for most of the cases covered by the verification.

Additional information

Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:

Mathematics - full-time second-cycle studies

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of WM-MA-AIN in USOSweb