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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00224724%21RIV15-MSM-21230___
rdf:type
skos:Concept n14:Vysledek
rdfs:seeAlso
https://ojs.cvut.cz/ojs/index.php/ap/article/view/AP.2014.54.0367
dcterms:description
This paper presents first steps towards evolutionary design of complex autonomous systems. The approach is inspired by modularity of human brain and principles of evolution. Rather than evolving neural networks or neural-based systems, the approach focuses on evolving hybrid networks composed of heterogeneous sub-systems implementing various algorithms/behaviors. Currently, the evolutionary techniques are used to optimize weights between predefined blocks (so called Neural Modules) in order to find an agent architecture appropriate for given task. The framework, together with the simulator of such systems is presented. Then, examples of agent architectures represented as hybrid networks are presented. One architecture is hand-designed and one is automatically optimized by means of Evolutionary Algorithm. Even on such a simple experiment, it can be observed how the evolution is able to pick-up unexpected attributes of the task and exploit them when designing new architecture. This paper presents first steps towards evolutionary design of complex autonomous systems. The approach is inspired by modularity of human brain and principles of evolution. Rather than evolving neural networks or neural-based systems, the approach focuses on evolving hybrid networks composed of heterogeneous sub-systems implementing various algorithms/behaviors. Currently, the evolutionary techniques are used to optimize weights between predefined blocks (so called Neural Modules) in order to find an agent architecture appropriate for given task. The framework, together with the simulator of such systems is presented. Then, examples of agent architectures represented as hybrid networks are presented. One architecture is hand-designed and one is automatically optimized by means of Evolutionary Algorithm. Even on such a simple experiment, it can be observed how the evolution is able to pick-up unexpected attributes of the task and exploit them when designing new architecture.
dcterms:title
Towards Evolutionary Design of Complex Systems Inspired by Nature Towards Evolutionary Design of Complex Systems Inspired by Nature
skos:prefLabel
Towards Evolutionary Design of Complex Systems Inspired by Nature Towards Evolutionary Design of Complex Systems Inspired by Nature
skos:notation
RIV/68407700:21230/14:00224724!RIV15-MSM-21230___
n3:aktivita
n6:S
n3:aktivity
S
n3:cisloPeriodika
5
n3:dodaniDat
n15:2015
n3:domaciTvurceVysledku
n4:4407822 n4:4022300
n3:druhVysledku
n12:J
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n17:predkladatel
n3:idSjednocenehoVysledku
50821
n3:idVysledku
RIV/68407700:21230/14:00224724
n3:jazykVysledku
n11:eng
n3:klicovaSlova
Agent; Architecture; Artificial Life; Creature; Behavior; Hybrid; Neural Network; Evolution
n3:klicoveSlovo
n5:Creature n5:Behavior n5:Architecture n5:Neural%20Network n5:Agent n5:Artificial%20Life n5:Evolution n5:Hybrid
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[A32C50A5B384]
n3:nazevZdroje
Acta Polytechnica
n3:obor
n8:JC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n15:2014
n3:svazekPeriodika
54
n3:tvurceVysledku
Nahodil, Pavel Vítků, Jaroslav
s:issn
1210-2709
s:numberOfPages
11
n13:doi
10.14311/AP.2014.54.0367
n16:organizacniJednotka
21230