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W. B. Langdon, R. Poli
School of Computer Science,
The University of Birmingham,
Birmingham B15 2TT, UK
{W.B.Langdon,R.Poli}@cs.bham.ac.uk
http://www.cs.bham.ac.uk/wbl, rmp
Tel: +44 (0) 121 414 4791,
Fax: +44 (0) 121 414 4281
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**Keywords:** genetic programming, structural complexity, introns,
Occam's Razor, MDL, Price's Theorem

The problem of evolving an artificial ant to follow the Santa Fe trail is used to study the well known genetic programming feature of growth in solution length. Known variously as ``bloat'', ``fluff'' and increasing ``structural complexity'', this is often described in terms of increasing ``redundancy'' in the code caused by ``introns''.

Comparison between runs with and without fitness selection pressure, backed by Price's Theorem, shows the tendency for solutions to grow in size is caused by fitness based selection. We argue that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. With simple static evaluation search converges to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long representations of a given solution than short ones. Thus in search (without a length bias) we expect longer representations to occur more often and so representation length to tend to increase. That is fitness based selection leads to bloat.

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- Introduction
- Bloat in Variable Length Representations
- The Artificial Ant Problem
- GP Parameters
- Results
- Discussion
- Conclusions
- Acknowledgements
- References
- About this document ...

Tue Jun 10 12:12:55 BST 1997