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Published byBenjamin Kerr Modified over 2 years ago

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JavaGenes: Evolving Graphs Al Globus, Veridian MRJ Technology Solutions, Inc. John Lawton, UCSC Todd Wipke, UCSC Sean Atsatt, Sierra Imaging,Inc.

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Crossover abcd wxyz abyz wxcd Strings Trees Graphs

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Graph Crossover Rip Two Parents Apart Combine into a Child

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Molecule Fitness Function All-pairs-shortest-path (APSP) distance Assign extended types to each atom = (element, |single bonds|, |double bonds|, |triple bonds|) Find shortest bond path between each pair of atoms Create bag with one item per pair of atoms –item = (type1, type2, path length) –bag = set with repeated items Tanimoto distance = |intersection| / |union|

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Evolving Molecules benzene purine cubane diazepam cholesterol morphine NA size median shortest failures(%)

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Circuits Directed graphs More node types (and, nand, or, nor, xor, nxor, plus initial 0 or 1 for each type) Exactly one input and one output node per graph Crossover generates unconnected circuits on very rare occasion Fitness function: number of right answers on 100 randomly generated inputs Serial logic (one bit input and output at each step) Logic simulator assumes all devices require unit time

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Circuits name size median shortest failures(%) parity delay add200NA 100

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Conclusions Evolving graphs using genetic operators is possible Molecules and circuits have been evolved Molecular evolution is reasonably successful Non-trivial circuits have yet to be evolved This is a new technique that will require substantial refinement open source in the near future Where self-assembly problems can be cast as graphs JavaGenes may be of some utility

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