Genetic Algorithms and Genetic Programming (VS1) (U02402)

? Credit Points : 10 ? SCQF Level : 9 ? Acronym : INF-3-GAGP-V

This course teaches you about genetic algorithms (GAs), genetic programming (GP) and other such evolutionary computing (EC) ideas based on the idea of solving problems through simulated evolution. These techniques are useful for searching very large spaces. For example, they can be used to search huge parameter spaces in engineering design and spaces of possible schedules in scheduling. However, they can also be used to search for rules and rule sets, for data mining, for good feed-forward or recurrent neural nets and so on. The idea of evolving, rather than designing, algorithms and controllers is especially appealing in AI. The module will also introduce other biologically inspired algorithms, particularly Ant Colony Optimisation methods.

In this course, you will learn about:
- The practicalities of GAs, GP and EC: how to design an appropriate evolutionary algorithm.
- Some of the underlying theory: how such algorithms work, and what is provable about them.
- Issues of experimental design: how to decide whether it works well.
- Current commercial applications.
- Current research directions.

Entry Requirements

? This course is only available to part year visiting students.

? This course is a variant of the following course : U01910

? Pre-requisites : Successful completion of Year 2 of an Informatics Single or Combined Degree, or equivalent by permission of the School. The course will involve a modest amount of mathematics in a few places, mainly basic probability and a little statistics.

Subject Areas

Home subject area

Bioinformatics, (School of Informatics, Schedule O)

Other subject areas

Intelligent Robotics, (School of Informatics, Schedule O)

Learning from Data, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : 3rd year

? Delivery Period : Semester 1 (Blocks 1-2)

? Contact Teaching Time : 3 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
22/09/2006	12:10	13:00	Lecture Theatre 1, Dan Rutherford Building	KB

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	12:10	13:00	KB
Lecture	Friday	12:10	13:00	KB

Summary of Intended Learning Outcomes

Understanding of evolutionary computation techniques and their broad applicability to a range of hard problems in search, optimisation and machine learning.

To know when an evolutionary technique is applicable, which one to choose and how to evaluate the results.

To know how to apply an evolutionary technique to a real problem and how to choose the parameters for optimal performance.

Matching techniques with problems, evaluating results, tuning parameters, creating algorithms using inspiration from natural systems.