Binary representation in gene expression programming: towards a better scalability

Authors:
Jose Garcia Moreno-Torres;Xavier Llora;David E. Goldberg
Affiliations:
University of Illinois at Urbana-Champaign, Urbana, IL, USA;University of Illinois at Urbana-Champaign, Urbana, IL, USA;University of Illinois at Urbana-Champaign, Urbana, IL, USA
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 2
Cited 0

The Design of Innovation: Lessons from and for Competent Genetic Algorithms

The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)

Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)

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Abstract

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 paliate the computation requirements needed. A theoretical reasoning behind the proposed representation is provided, along with empirical validation.