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The Proportional Genetic Algorithm: Gene Expression in a Genetic Algorithm

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Abstract

We introduce a genetic algorithm (GA) with a new representation method which we call the proportional GA (PGA). The PGA is a multi-character GA that relies on the existence or non-existence of genes to determine the information that is expressed. The information represented by a PGA individual depends only on what is present on the individual and not on the order in which it is present. As a result, the order of the encoded information is free to evolve in response factors other than the value of the solution, for example, in response to the identification and formation of building blocks. The PGA is also able to dynamically evolve the resolution of encoded information. In this paper, we describe our motivations for developing this representation and provide a detailed description of a PGA along with discussion of its benefits and drawbacks. We compare the behavior of a PGA with that of a canonical GA (CGA) and discuss conclusions and future work based on these preliminary studies.

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  1. School of EECS, University of Central Florida, P.O. Box 162362, Orlando, FL, 32816-2362

    Annie S. Wu & Ivan Garibay

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  1. Annie S. Wu
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  2. Ivan Garibay
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Wu, A.S., Garibay, I. The Proportional Genetic Algorithm: Gene Expression in a Genetic Algorithm. Genet Program Evolvable Mach 3, 157–192 (2002). https://doi.org/10.1023/A:1015531909333

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