Module also offered within study programmes:
General information:
Name:
Genetic and evolutionary algorithms in engineering
Course of study:
2013/2014
Code:
RMS-1-609-s
Faculty of:
Mechanical Engineering and Robotics
Study level:
First-cycle studies
Specialty:
-
Field of study:
Mechatronics with English as instruction languagege
Semester:
6
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
dr inż. Gibiec Mariusz (mgi@agh.edu.pl)
Academic teachers:
dr inż. Gibiec Mariusz (mgi@agh.edu.pl)
Module summary

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence
M_K001 awareness of the responsibility for own work and readiness to comply with the rules of team work and accepting responsibility for tasks performed collectively MS1A_K04 Execution of laboratory classes
Skills
M_U001 ability to assess the type of the mechatronic problem and to choose the aproppiate artificial intelligence method for solving it MS1A_U10, MS1A_U12 Execution of laboratory classes,
Test results
M_U002 ability to formulate the mechatronic problem and to design and build the artificial intelligence tool that solves it. MS1A_U14, MS1A_U10, MS1A_U12, MS1A_U08 Execution of laboratory classes,
Test results
M_U003 ability to code properly input data and to adjust parameters of artificial intelligence methods for solving problems of optimization, classification and pattern recognition and to aid the designing process with the use of these methods MS1A_U12, MS1A_U08
Knowledge
M_W001 knowledge of the methods and techniques of the artificial intelligence and the background of their formulation; knowledge of their utilisation for the engineering problems solving MS1A_W12 Execution of laboratory classes,
Test results
M_W002 knowledge and understanding of methodology of the artificial intelligence systems applications for the purposes of designing and building mechatronic devices; knowledge of computer tools for the design and simulation of artificial intelligent systems MS1A_W12 Execution of laboratory classes,
Test results
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Others
Zaj. terenowe
Zaj. warsztatowe
E-learning
Social competence
M_K001 awareness of the responsibility for own work and readiness to comply with the rules of team work and accepting responsibility for tasks performed collectively - - + - - - - - - - -
Skills
M_U001 ability to assess the type of the mechatronic problem and to choose the aproppiate artificial intelligence method for solving it + - + - - - - - - - -
M_U002 ability to formulate the mechatronic problem and to design and build the artificial intelligence tool that solves it. + - + - - - - - - - -
M_U003 ability to code properly input data and to adjust parameters of artificial intelligence methods for solving problems of optimization, classification and pattern recognition and to aid the designing process with the use of these methods + - + - - - - - - - -
Knowledge
M_W001 knowledge of the methods and techniques of the artificial intelligence and the background of their formulation; knowledge of their utilisation for the engineering problems solving + - - - - - - - - - -
M_W002 knowledge and understanding of methodology of the artificial intelligence systems applications for the purposes of designing and building mechatronic devices; knowledge of computer tools for the design and simulation of artificial intelligent systems + - - - - - - - - - -
Module content
Lectures:

1. Introduction to the artificial intelligence methods
2. Genetic Algorithms – theoretical bases
3. A Survey of Genetic Algorithms Applied to Academic and Industrial Test Cases
4. Using Genetic Algorithms for Optimization
5. Genetic optimization of designing process
6. Evolutionary Algorithms – theoretical bases
7. A Survey of Evolutionary Algorithms Applied to Academic and Industrial Test Cases
8. Genetic and Evolutionary Algorithms Application to Artificial Neural Networks Development

Laboratory classes:

An Introduction to Genetic and Evolutionary Computation – a software survey
Three Elements of Representations for Genetic and Evolutionary Algorithms
Analysis and Design of Representations for Trees.
Using Genetic Algorithms for Optimization
Genetic Algorithms for the Traveling Salesman Problem
Generator Scheduling in Power Systems by Genetic Algorithm
Genetic Algorithm as a Tool for Solving Electrical Engineering Problems
Genetic Algorithms in Shape Optimization
Evolutionary Approaches to Clustering
Partitioning 3-D Unstructured Grids Using Evolutionary Algorithms
Genetic and Evolutionary Algorithms Application to Artificial Neural Networks Development

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 80 h
Module ECTS credits 3 ECTS
Examination or Final test 1 h
Realization of independently performed tasks 15 h
Preparation of a report, presentation, written work, etc. 14 h
Participation in laboratory classes 30 h
Participation in lectures 15 h
Preparation for classes 5 h
Additional information
Method of calculating the final grade:

Weighted sum of mark from the test and marks from laboratory reports

Prerequisites and additional requirements:

Prerequisites and additional requirements not specified

Recommended literature and teaching resources:

Genetic algorithms in engineering and computer science / ed. by G. Winter [et al.].
Genetic algorithms in search, optimization, and machine learning / David E. Goldberg.
Genetic algorithms in optimisation, simulation and modelling / ed. by J. Stender, E. Hillebrand and J. Kingdon.
Evolutionary algorithms for single and multicriteria design optimization / Andrzej Osyczka.

Scientific publications of module course instructors related to the topic of the module:

Additional scientific publications not specified

Additional information:

None