Admission requirements
Assumed prior knowledge
The prerequisites of the course include real analysis, linear algebra, probability theory, stochastic processes, basic knowledge on manifold, and some selected topics from mathematical statistics (e.g., parametric estimation and information geometry).
Description
Evolutionary Computation is a field of computer science dealing with algorithms gleaned from the model of organic evolution – so-called evolutionary algorithms. The idea is to let the computer evolve solutions to problems rather than trying to “calculate” them.
Evolutionary algorithms do this by using the fundamental principles of evolution such as, for example, selection, mutation and recombination among a population of simulated individuals. The evolutionary approach is used today in a variety of application areas for solving problems that require intelligent behaviour, adaptive learning and optimization. These fields include e.g. engineering optimization, artificial life, automatic programming, autonomous agents, and machine learning, since optimization algorithms are a core component of many machine learning approaches.
The course focuses on the fundamentals of biological evolution as the underlying motivation, the main variants of evolutionary algorithms (genetic algorithms and evolution strategies), application examples, and some outlook into related aspects of evolutionary computation.
Table of Contents:
- Introduction
- Biological Evolution
- Mathematical and stochastic optimization
- Evolutionary Algorithms
- Genetic Algorithms
- Evolution Strategies
- Genetic Programming
- Covariance Matrix Adaptation Evolution Strategy and Natural Evolution Strategy
- Neural Evolution
Course objectives
The course gives a comprehensive overview of the field through a series of lectures and exercises. In addition, a practical application exercise of evolutionary computation is given to the students, who are expected to run experiments and write a report about the experiment and the results obtained. This report will be written in scientific paper format, to gain some experience in scientific writing.
Provided that the quality of results is good, we will encourage and help the authors of the best paper to submit it to a scientific conference.
Learn the main algorithms in the field, in particular genetic algorithms and evolutionary strategies.
Understand the underlying principles of evolutionary computation.
Learn the theoretical foundations of evolutionary computation.
Apply the algorithms to some application area and obtain some practical experience.
Learn about applications in science and industry.
Learn how to write a short scientific paper in evolutionary computation.
Timetable
The most recent timetable can be found at the Computer Science (MSc) student website.
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Lectures and practical assignment sessions.
Course load
Hours of Study: 168 (= 6 EC)
Lectures: 26
Practical assignment sessions: 10
Practical work: 48
Report: 48
Exam and preparation: 36
Assessment method
The final grade is a combination of grades for (1) the written exam (60%) and (2) the report about the practical assignment (40%).
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
Reading list
The following literature is recommended but not mandatory for the course:
Bäck, Thomas, Christophe Foussette, and Peter Krause. Contemporary evolution strategies. Vol. 86. Berlin: Springer, 2013.
Emmerich, Michael, Ofer M. Shir, and Hao Wang. "Evolution Strategies." (2018): 89-119.
A.E. Eiben, J.E. Smith: Introduction to Evolutionary Computing, Springer, Berlin, 2015. ISBN 978-3-662-44874-8
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Lecturers: dr. Hao Wang
Website: See course page on Brightspace.
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.