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.
PGA example
Key results
Publications
Ivan Garibay, Annie S. Wu, and Ozlem Garibay (2005).
Emergence of genomic self-similarity in location independent
representations.
Accepted for publication in
Genetic Programming and Evolvable Machines.
Annie S. Wu and Ivan I. Garibay (2002)
The proportional genetic algorithm:
Gene expression in a genetic algorithm.
Genetic Programming and Evolvable Machines 3:2 157-192.
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Annie S. Wu and Ivan I. Garibay (2004)
Intelligent automated control of life support systems using
proportional representations.
IEEE Transactions on Systems, Man, and Cybernetics, Part B,
pp. 1423-1434, June 2004.
