There is an increasing use of nature-inspired computational techniques in computer science. These include the use of biology as a source of inspiration for solving computational problems, such as developments in evolutionary algorithms and swarm intelligence. Similarly, there is now also an increasing interest in understanding how biological, chemical and other natural systems compute, and how this could be exploited for practical applications. It is therefore proposed to allow students the opportunity to become exposed to these types of methods for use in their later careers.
Total contact hours: 22
Private study hours: 128
Total study hours: 150
Main assessment methods
Time limited assessment (about 15 hours) (20%)
One short essay (about 1,000 words) (20%)
Examination (60%)
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.
Eiben, AE, Smith, JE. (2015) Introduction to Evolutionary Computing, 2nd Edition. Springer.
Dorigo, M. and Stutzle, T. (2004) Ant Colony Optimization, MIT Press.
Barnes, DJ, Chu, D. (2010) Introduction to Modeling for Biosciences, Springer
See the library reading list for this module (Canterbury)
1. describe what is meant by a natural computation paradigm, list a number of natural computing paradigms and give a brief description of each together with some examples of their (actual or potential) applications.
2. select the appropriate technique for a particular problem from a set of problem-solving heuristics based on these natural computing paradigms, and to be able to justify this choice based on a knowledge of the properties and potential of these methods.
3. analyse phenomena from the natural world from the point of view of their being computational systems. To be able to take these phenomena and distinguish between the features which are important for computational problem solving and those that are merely a fact of their realization in the natural world.
4. exploit library and online resources to support investigations into these areas.
5. critically evaluate advanced natural computation techniques based on current research.
6. have a comprehensive understanding on how to design natural computational techniques for addressing specific characteristics of a particular complex optimisation problem.
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