Undergraduate Course: Natural Computing (INFR11161)
This course will be closed from 31 July 2024
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This module teaches you about bio-inspired algorithms for optimisation and search problems. The algorithms are based on simulated evolution (including Genetic algorithms and Genetic programming), particle swarm optimisation, ant colony optimisation as well as systems made of membranes or biochemical reactions among molecules. These techniques are useful for searching very large spaces. For example, they can be used to search large 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. In a similar way it is tempting to use the intrinsic dynamics of real systems consisting e.g. of quadrillions of molecules to perform computations for us. The course includes technical discussions about the applicability and a number of practical applications of the algorithms.
In this module, students will learn about
- The practicalities of natural computing methods: How to design algorithms for particular classes of problems.
- Some of the underlying theory: How such algorithms work and what is provable about them.
- Issues of experimental design: How to decide whether an metaheuristic algorithm works well.
- Current commercial applications.
- Current research directions. |
| Course description |
The lectures will cover the following subjects:
- Computational aspects of animal behaviour and of biological, chemical or physical systems
- Genetic and Evolutionary Algorithms: Selection, recombination and mutation, fitness and objective functions
- Swarm intelligence, particle swarms, differential evolution, robot swarms
- Theory: the schema theorem and its flaws; convergence, statistical mechanics approaches
- Comparisons among various metaheuristic algorithms, No-Free-Lunch theorems
- Hybrid, hyperheuristic, and memetic algorithms
- Multi-objective optimisation
- Genetic programming
- Applications such as engineering optimisation; scheduling; data-mining; neural net design
- Experimental issues: Design and analysis of sets of experiments
Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Data Structures and Algorithms, Simulation and Modelling
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser (lecturer).
Mathematical background, at the level of undergraduate informatics, particularly linear algebra (vector spaces, subspaces, eigenvalues), calculus (partial derivation, extrema, concavity) and statistics (mean and variance, hypotheses testing, principal component analysis). Some programming will be required. |
Information for Visiting Students
| Pre-requisites | As above. |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- understand natural computation techniques in theory and in their broad applicability to a range of hard problems in search, optimisation and machine learning
- to know when a natural computing technique is applicable, which one to choose and how to evaluate the results
- to know how to apply a natural computing technique to a real problem and how to choose the parameters for optimal performance
- match techniques with problems, evaluating results, tuning parameters, creating (memetic) algorithms by evolution
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Reading List
- Melanie Mitchell: An Introduction to Genetic Algorithms. MIT Press, 1998.
- Xin-She Yang: Nature-Inspired Metaheuristic Algorithms. Luniver, 2010.
- Brabazon, O'Neill, McGarraghy: Natural Computing Algorithms. Springer, 2015. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | NAT,Natural Computing |
Contacts
| Course organiser | Dr Michael Herrmann
Tel: (0131 6)51 7177
Email: Michael.Herrmann@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 2701
Email: lindsay.seal@ed.ac.uk |
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