Evolving robot sub-behaviour modules using Gene Expression Programming
Created by W.Langdon from
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- @Article{Mwaura:2014:GPEM,
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author = "Jonathan Mwaura and Ed Keedwell",
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title = "Evolving robot sub-behaviour modules using Gene
Expression Programming",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2015",
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volume = "16",
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number = "2",
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pages = "95--131",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, Subsumption architecture,
Layered learning, Evolutionary robotics, Robot
behaviour coordination",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-014-9229-x",
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size = "37 pages",
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abstract = "Many approaches to AI in robotics use a multi-layered
approach to determine levels of behaviour from basic
operations to goal-directed behaviour, the most
well-known of which is the subsumption architecture. In
this paper, the performances of the unigenic Gene
Expression Programming (ugGEP) and multigenic GEP
(mgGEP) in evolving robot controllers for a wall
following robot are analysed. Additionally, the paper
introduces Regulatory Multigenic Gene Expression
Programming, a new evolutionary technique that can be
used to automatically evolve modularity in robot
behaviour. The proposed technique extends the mgGEP
algorithm, by incorporating a regulatory gene as part
of the GEP chromosome. The regulatory gene, just as in
systems biology, determines which of the genes in the
chromosome to express and therefore how the controller
solves the problem. In the initial experiments, the
proposed algorithm is implemented for a robot wall
following problem and the results compared to that of
ugGEP and mgGEP. In addition to the wall following
behaviour, a robot foraging behaviour is implemented
with the aim of investigating whether the position of a
specific module (sub-expression tree) in the overall
expression tree is of importance when coding for a
problem.",
- }
Genetic Programming entries for
Jonathan Mwaura
Ed Keedwell
Citations