Heuristic Optimisation

5 ECTS credits
125 h study time

Offer 1 with catalog number 4014866FNR for all students in the 2nd semester at a (F) Master - specialised level.

Semester

2nd semester

Enrollment based on exam contract

Impossible

Grading method

Grading (scale from 0 to 20)

Can retake in second session

Yes

Taught in

English

Partnership Agreement

Under agreement for exchange of courses

Faculty

Faculty of Sciences and Bioengineering Sciences

Department

Computer Science

External partners

Université libre de Bruxelles

Educational team

Decaan WE (course titular)

Activities and contact hours

26 contact hours Lecture
13 contact hours Seminar, Exercises or Practicals
13 contact hours Independent or External Form of Study

Course Content

Computationally hard problems arise in many relevant application areas of computational intelligence such as computer science, operations research, bioinformatics, and engineering. For many such problems, heuristic search techniques have been established as the most successful methods. In this course I will introduce and discuss heuristic optimization techniques with a main focus on stochastic local search techniques, which are the most relevant heuristic techniques. The course will illustrate the application principles of these algorithms using a number of example applications ranging from rather simple problems of more academic interest to more complex problems from real applications. A significant focus in the course will be also on relevant techniques for the empirical evaluation of heuristic optimization algorithms and the issues that arise in their development. Hands-on experience with these algorithmic techniques will be gained in accompanying practical exercises.

Additional info

ULB Course

Learning Outcomes

Algemene competenties

The main objective is to give students theoretical and practical knowledge of how to tackle effectively difficult optimization problems with heuristic techniques, in particular, stochastic local search methods. In more detail, the goals are

-Learn about heuristic optimization techniques

-Learn how these can be used to tackle combinatorial optimization problems

-Learn how to analyze heuristic algorithms empirically.

-Obtain hands-on experience with the implementation and the application of heuristic techniques.

Grading

The final grade is composed based on the following categories:
Oral Exam determines 100% of the final mark.

Within the Oral Exam category, the following assignments need to be completed:

oral examination with a relative weight of 1 which comprises 100% of the final mark.

Additional info regarding evaluation

Oral examination

Allowed unsatisfactory mark

The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context