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dc.contributor.authorNichele, Stefano
dc.contributor.authorOse, Mathias Berild
dc.contributor.authorRisi, Sebastian
dc.contributor.authorTufte, Gunnar
dc.date.accessioned2018-01-26T14:56:34Z
dc.date.available2018-01-26T14:56:34Z
dc.date.created2017-12-11T15:10:16Z
dc.date.issued2017
dc.identifier.issn2379-8920
dc.identifier.urihttp://hdl.handle.net/11250/2479992
dc.description.abstractCellular Automata (CA) are a remarkable example of morphogenetic system, where cells grow and self-organise through local interactions. CA have been used as abstractions of biological development and artificial life. Such systems have been able to show properties that are often desirable but difficult to achieve in engineered systems, e.g. morphogenesis and replication of regular patterns without any form of centralized coordination. However, cellular systems are hard to program (i.e. evolve) and control, especially when the number of cell states and neighbourhood increase. In this paper, we propose a new principle of morphogenesis based on Compositional Pattern Producing Networks (CPPNs), an abstraction of development that has been able to produce complex structural motifs without local interactions. CPPNs are used as Cellular Automata genotypes and evolved with a NeuroEvolution of Augmenting Topologies (NEAT) algorithm. This allows complexification of genomes throughout evolution with phenotypes emerging from self-organisation through development based on local interactions. In this paper, the problems of 2D pattern morphogenesis and replication are investigated. Results show that CA-NEAT is an appropriate means of approaching cellular systems engineering, especially for future applications where natural levels of complexity are targeted. We argue that CA-NEAT could provide a valuable mapping for morphogenetic systems, beyond cellular automata systems, where development through local interactions is desired.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.relation.urihttp://ieeexplore.ieee.org/abstract/document/8004527/
dc.titleCA-NEAT: Evolved Compositional Pattern Producing Networks for Cellular Automata Morphogenesis and Replicationnb_NO
dc.typeJournal articlenb_NO
dc.description.versionsubmittedVersionnb_NO
dc.source.volumePPnb_NO
dc.source.journalIEEE Transactions on Cognitive and Developmental Systemsnb_NO
dc.source.issue99nb_NO
dc.identifier.doi10.1109/TCDS.2017.2737082
dc.identifier.cristin1525789
dc.description.localcode© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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