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Introduction to Genetic Programming


Status Archive
School National Graduate School in Computer Science (CUGS)
Division ADIT
Owner Nahid Shahmehri

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Course plan


2 days intensive course

Recommended for

PhD students

The course was last given

New course


Genetic Programming (GP) is an important machine learning technique. It is a powerful, domain independent paradigm that can be used not only for optimization problems, but for invention and discovery. In this course, we will go from the motivations behind GP, to the inner workings of the algorithms to actual software that you can use out-of-the-box.


MSc in mathematics or computer science/engineering.


Genetic Programming (GP)
Evolutionary Algorithms
Problem solving with GP
Software for GP


This is an intensive course, consisting of two days of lectures and practical exercises. The participants will have the opportunity to do the exercises afterwards and apply GP to their own research area.
Estimated own work is 8 to 16 hrs to complete. A selection of datasets is provided, and the participants can chose one to apply GP techniques to. They can either use off-the-shelf software, their own software or modifying existing software. The task will involve finding suitable parameters for the algorithm, performing the training and then evaluating the quality of the found solutions. Assessment will be based on the quality of the scientific report, which should be written in the style of a conference paper.


A Field Guide to Genetic Programming, by W. B. Langdon.
The e-book is available free of charge.


See the course plan.


Dr Simon Harding (local examiner: Nahid Shahmehri)


Assessment will be by practical work. Students will be asked to implement software to solve a typical GP problem (e.g. time series prediction) and write a short, scientific report on their findings.


3 hp

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Registrations are binding.

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