Implicit Context Representation for Evolutionary Computation
- Members: Michael Lones, Andy Tyrrell, Steve Smith
- Website: http://www-users.york.ac.uk/~mal503/implicit.htm
- Start Date: October 2000
Implicit context is a novel approach to representing evolving structures in which structural components describe their interactions using functional descriptions of other components. In conventional structural representations, these interactions are described using positional information. However, positional information is not generally conserved between solutions and between generations, leading to disruptive effects when genetic operators are applied. This can be seen in genetic programming, where sub-tree crossover does not typically carry out useful behaviour. Enzyme genetic programming is a form of genetic programming which uses implicit context representation, and has shown behavioural and performance benefits when applied to a range of problems. More recently, we have also shown how implicit context may be implemented within cartesian genetic programming. Application domains include digital circuit design, image filter design, and finite state automata induction.
Recent Publications
Positional Independence and Recombination in Cartesian Genetic Programming
X. Cai, S. L. Smith and A. M. Tyrrell, European Conference on Genetic Programming (EuroGP), LNCS 3905:351-360, 2006.
An Implicit Context Representation for Evolving Image Processing Filters
S. L. Smith, S. Leggett and A. M. Tyrrell, EvoWorkshops2005, LNCS 3449:407-416, 2005.
Enzyme Genetic Programming: Modelling Biological Evolvability in Genetic Programming
Michael Adam Lones, Ph.D. Thesis, Department of Electronics, University of York, 2004.
Modelling Biological Evolvability: Implicit Context and Variation Filtering in Enzyme Genetic Programming
M. A. Lones and A. M. Tyrrell, BioSystems 76(1-3):229-238, August-October 2004.