Evolution, Generality and Robustness of Emerged Surrounding Behavior in Continuous Predators-Prey Pursuit Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{tanev:2005:GPEM,
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author = "Ivan Tanev and Michael Brzozowski and
Katsunori Shimohara",
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title = "Evolution, Generality and Robustness of Emerged
Surrounding Behavior in Continuous Predators-Prey
Pursuit Problem",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2005",
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volume = "6",
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number = "3",
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pages = "301--318",
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month = sep,
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note = "Published online: 25 August 2005",
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keywords = "genetic algorithms, genetic programming, emergence,
multi agent systems, surrounding behaviour,
strongly-typed genetic programming STGP",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-005-2989-6",
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size = "18 pages",
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abstract = "We present the result of our work on the use of
strongly typed genetic programming with exception
handling capabilities for the evolution of surrounding
behaviour of agents situated in an inherently
cooperative environment. The predators-prey pursuit
problem is used to verify our hypothesis that
relatively complex surrounding behavior may emerge from
simple, implicit, locally defined, and therefore
scalable interactions between the predator agents.
Proposing two different communication mechanisms ((i)
simple, basic mechanism of implicit interaction, and
(ii) explicit communications among the predator agents)
we present a comparative analysis of the implications
of these communication mechanisms on evolution,
generality and robustness of the emerged surrounding
behaviour. We demonstrate that relatively
complex-surrounding behaviour emerges even from
implicit, proximity-defined interactions among the
agents. Although the basic model offers the benefits of
simplicity and scalability, compared to the enhanced
model of explicit communications among the agents, it
features increased computational effort and inferior
generality and robustness of agents' emergent
surrounding behaviour when the team of predator agents
is evolved in noiseless environment and then tested in
noisy and uncertain environment. Evolution in noisy
environment virtually equalises the robustness and
generality characteristics of both models. For both
models however the increase of noise levels during the
evolution is associated with evolving solutions, which
are more robust to noise but less general to new,
unknown initial situations.",
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notes = "DOM XML, explicit fitness parsimony preasure (anti
bloat)",
- }
Genetic Programming entries for
Ivan T Tanev
Michael Brzozowski
Katsunori Shimohara
Citations