TIES677 COM3: Data-driven optimization via search heuristics (4 cr)

Study level:
Advanced studies
Grading scale:
Pass - fail
Responsible organisation:
Faculty of Information Technology
Curriculum periods:
2017-2018, 2018-2019, 2019-2020



The course covers the emerging topic around data-driven optimization, which deals with problems that vary from the default formal problem description consisting of equations and functions. We will discuss a range of examples of data-driven problems covering both problems relying on simulations and/or physical experiments in the evaluation of solutions. The application of search heuristics, such as evolutionary algorithms, has become crucial in this domain. This course will introduce students to the core search heuristics and real-world challenges that need to be accounted for when tackling data-driven optimization problems. The following topics will be covered in the course:

- Fundamentals of optimization, decision making, and search heuristics
- Simulation meets optimization
- Experimental and expensive optimization
- Using data only to conceptualize and optimize a problem
- Uncertainty and constraint-handling
- Multiobjective and mixed-integer optimization
- Data-driven real-world applications of search heuristics
* Drug discovery
* Design of manufacturing processes
* Instrument setup tuning
* Portfolio optimization
* Allocation of computational resources
* Production planning

Learning outcomes

Data-driven optimization via search heuristics

Description of prerequisites

The course will build on basic concepts in probability, statistics, and discrete mathematics. It would be suitable for anyone with a numerate background who has an interest in learning about optimization and/or machine learning. Programming experience is beneficial but not a pre-requisite.

Completion methods

Method 1

Select all marked parts
Parts of the completion methods

Teaching (4 cr)

Participation in teaching
Grading scale:
Pass - fail
No published teaching