TIES451 Selected Topics in Soft Computing (4 cr)

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Study level:
Advanced studies
Grading scale:
0-5
Language:
English
Responsible organisation:
Faculty of Information Technology
Curriculum periods:
2017-2018, 2018-2019, 2019-2020

Description

Content

Evolutionary computation is one of the most important components in soft computing. They draw inspiration from nature's problem solving tools and evolution in specific. In this course we shall take a journey through different aspects of evolutionary computation and explore different important algorithms proposed by various researchers from all over the world. Evolutionary computation are widely used for both search and optimization. In particular it is widely used to solve optimization problems (single and multi-objective) when no explicit gradient information is available. This course lays foundation to more advanced study in evolutionary computation and their applications in optimization of large scale industrial optimization problems, designing AI for games, robotics etc.

Completion methods

Quiz, Assignments and Examination

Assessment details

Six weekly exercises (80%) and Quiz (20%) (held in the class).

Learning outcomes

Upon completion of the course, students will have a grasp on the basics of evolutionary algorithms. They can identify areas where the evolutionary algorithms can be applied and successfully use them to solve problems arising in industry and research.

Description of prerequisites

Profeciency in programming in any language and some basic math.

Completion methods

Method 1

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Parts of the completion methods
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Teaching (4 cr)

Type:
Participation in teaching
Grading scale:
0-5
Language:
English
No published teaching