Personal Web Page:

 http://www.cs.mdx.ac.uk/staffpages/ianm/home.html

Qualifications

BSc, PhD

Publications:

  • A. Agrawal and I. Mitchell . Selection enthusiasm. In 6th Int. Conf. on Simulated Evolution and Learning, SEAL'06. Lecture Notes in Computer Science (LNCS) Springer-Verlag, 2006.
  • A. Agrawal, I. Mitchell, P. Passmore and I. Litovski (2005) "Dynamics in Proportionate Selection" ICANNGA05.
  • Mitchell, I. A Generic Representation for GAs. in sixth IASTED International Conference Artificial Intelligence¬†& Soft Computing, ASC 2002.
  • Huyck, C. and Mitchell, I. Cell Assemblies, Self Organising Maps and Hopfield Nets. In sixth International Conference on Cognitive and Neural Systems 2002.
  • Cairns P. A., Huyck C. R., Mitchell I. and Wu W. X. (2001). A comparison of Categorisation Algorithms for Predicting the Cellular Localization Sites of Proteins. DEXA 2001.
  • Bavan A.S.and Mitchell I. (2000) A Connectionist Inference Model for Pattern-Directed Knowledge Representation. In Journal on Expert Systems, 17(2), pp165-172, ISBN: 0266-4720.
  • Mitchell I.,Pocknell P., (2000), A temporal representations for GA and TSP, in Sixth LNCS on Parallel Problem Solving from Nature, PPSN VI, Springer-Verlag, vol. 1917, pp 651-660, ISBN: 3-540-41056-2.
  • MitchellI., (2000), Pocknell, P., Parallel Dilation Technique for TSP, in International Conference on Artificial Intelligence, ICAI 2000, pp 1413-1418, ISBN:1-892512-58-0.
  • Bavan A.S.and Mitchell I. (2000) A Connectionist Inference Model for Pattern-Directed Knowledge Representation. in Journal on Expert Systems, 17(2), pp165-172, ISBN: 0266-4720.
  • Mitchell I., Bevan, A.S., (1999), GSAAM: graph, sets and associative memories, Proc of the International conference on artificial intelligence, IC-AI '99, Las Vegas, USA.

 

  • BIS3051 - Commercial Web Design (Module Leader) 
  • BIS2216 - Forensic Computing Analysis
  • Keywords: Evolutionary Computation, Genetic Algorithms, Genetic Programming, XML.
  • Ian has implemented one representation for a genetic algorithm that finds near optimal solutions for NP-complete problems e.g. Maximal Cliques, Graph Colouring, etc. Briefly, this representation when producing offspring - via traditional genetic operators such as crossover and mutation - has no illegality and therefore requires no repair algorithms. Investigations continue to see if this representation can be exploited further and produce coevolutionary model that can enforce mutualism between two or more populations each trying to find near-optimal solutions for different problem domains.
  • Ian has implemented a Genetic Program, GP, that instead of using the traditional S-expressions uses an XML application, MathML. Using Koza's quintic and sextic polynomials the GP successfully regressed to produced exact replicas (ignoring bloat). As expected the amount of bloat was significantly reduced when using Langdon's "Size Fair and Homologous" crossover techniques, although there are some significant differences between S-expressions and MathML that require further research. Ian is looking for funding to investigate the use of GPs to generate other XML instances e.g. XSL/T, XSD, ChemML, XHTML, etc.

Research Supervision:

Director of Studies

Second Supervisor