Graph Crossover
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Misc{globus:2001:GECCOtr,
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author = "Al Globus and Sean Atsatt and John Lawton and
Todd Wipke",
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title = "Graph Crossover",
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howpublished = "www",
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year = "2000",
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month = "5 " # may,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://alglobus.net/NASAwork/papers/JavaGenes2/JavaGenesPaper.html",
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broken = "http://people.nas.nasa.gov/~globus/papers/JavaGenes2/JavaGenesPaper.html",
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size = "15 pages",
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abstract = "Most genetic algorithms use string or tree
representations. To apply genetic algorithms to graphs,
a good crossover operator is necessary. We have
developed a general-purpose, novel crossover operator
for directed and undirected graphs, and used this
operator to evolve molecules and circuits. Unlike
strings or trees, a single point in the representation
cannot divide every possible graph into two parts,
because graphs may contain cycles. Thus, the crossover
operator is non-trivial. A steady-state, tournament
selection genetic algorithm code (JavaGenes) was used
test the graph crossover operator. JavaGenes has
successfully evolved pharmaceutical drug molecules and
simple digital circuits. For example, morphine,
cholesterol, and diazepam were successfully evolved by
30-60percent of runs within 10,000 generations using a
population of 1000 molecules. Since representation
strongly affects genetic algorithm performance, adding
graphs to the evolutionary programmer's bag-of-tricks
should be beneficial. Also, since graph evolution
operates directly on the phenotype, genotype to
phenotype decoding is eliminated.",
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notes = "see \cite{globus:2001:GECCO}",
- }
Genetic Programming entries for
Al Globus
Sean Atsatt
John Lawton
Todd Wipke
Citations