---
abstract: 'The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so.'
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chapter: ~
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confdates: 'July 16-21, 2006'
conference: IEEE Congress on Evolutionary Computation
confloc: 'Vancouver, Canada'
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creators_id: []
creators_name:
- family: De Nardi
given: Renzo
honourific: ''
lineage: ''
- family: Togelius
given: Julian
honourific: ''
lineage: ''
- family: Holland
given: Owen
honourific: ''
lineage: ''
- family: Lucas
given: Simon M.
honourific: ''
lineage: ''
date: 2006
date_type: published
datestamp: 2006-10-15
department: ~
dir: disk0/00/00/52/22
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eprint_status: archive
eprintid: 5222
fileinfo: /style/images/fileicons/application_pdf.png;/5222/1/DeNardi2006Evolution.pdf
full_text_status: public
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ispublished: pub
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keywords: ~
lastmod: 2011-03-11 08:56:39
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metadata_visibility: show
note: ~
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pubdom: FALSE
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publisher: IEEE Press
refereed: TRUE
referencetext: ~
relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 17:08:08
subjects:
- comp-sci-mach-learn
- comp-sci-neural-nets
- comp-sci-robot
succeeds: ~
suggestions: ~
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thesistype: ~
title: 'Evolution of Neural Networks for Helicopter Control: Why Modularity Matters'
type: confpaper
userid: 4535
volume: ~