Searching Search Spaces: Meta-evolving a Geometric Encoding for Neural Networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{kunze:2024:CEC,
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author = "Tarek Kunze and Paul Templier and Dennis G. Wilson",
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title = "Searching Search Spaces: Meta-evolving a Geometric
Encoding for Neural Networks",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, Neurons, Genomics, Evolutionary
computation, Encoding, Bioinformatics, Biological
neural networks, evolution strategies, meta-evolution,
neural networks, reinforcement learning, policy
search",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612026",
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abstract = "In evolutionary policy search, neural networks are
usually represented using a direct mapping: each gene
encodes one network weight. Indirect encoding methods,
where each gene can encode for multiple weights,
shorten the genome to reduce the dimensions of the
search space and better exploit permutations and
symmetries. The Geometric Encoding for Neural network
Evolution (GENE) introduced an indirect encoding where
the weight of a connection is computed as the
(pseudo-)distance between the two linked neurons,
leading to a genome size growing linearly with the
number of genes instead of quadratically in direct
encoding. However GENE still relies on hand -crafted
distance functions with no prior optimisation. Here we
show that better performing distance functions can be
found for GENE using Cartesian Genetic Programming
(CGP) in a meta-evolution approach, hence optimising
the encoding to create a search space that is easier to
exploit. We show that GENE with a learnt function can
outperform both direct encoding and the hand-crafted
distances, generalizing on unseen problems, and we
study how the encoding impacts neural network
properties.",
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notes = "also known as \cite{10612026}
WCCI 2024",
- }
Genetic Programming entries for
Tarek Kunze
Paul Templier
Dennis G Wilson
Citations