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Binary Representation in Gene Expression Programming: Towards a Better Scalability

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3 Author(s)
Jose G. Moreno-Torres ; Illinois Genetic Algorithms Lab. (IlliGAL), Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA ; Xavier LlorĂ  ; David E. Goldberg

One of the main problems that arises when using gene expression programming (GEP) conditions in learning classifier systems is the increasing number of symbols present as the problem size grows. When doing model-building LCS, this issue limits the scalability of such a technique, due to the cost required. This paper proposes a binary representation of GEP chromosomes to palliate the computation requirements needed. A theoretical reasoning behind the proposed representation is provided, along with empirical validation.

Published in:

2009 Ninth International Conference on Intelligent Systems Design and Applications

Date of Conference:

Nov. 30 2009-Dec. 2 2009