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dc.contributor.authorNichele, Stefano
dc.date.accessioned2015-05-29T13:51:04Z
dc.date.available2015-05-29T13:51:04Z
dc.date.issued2015
dc.identifier.isbn978-82-326-0730-3
dc.identifier.isbn978-82-326-0731-0
dc.identifier.issn1503-8181
dc.identifier.urihttp://hdl.handle.net/11250/284329
dc.description.abstractMan-made systems, such as supercomputers and software, IT-infrastructures and networks of any kind, are continuously growing in size and complexity. As conventional top-down engineering techniques may have reached the limit of applicability, biological organisms have been able to evolve increasing levels of complexity. Such inherent biological complexity may be said to be open ended or unbounded. This is a result of a bottom-up emergent process which produced an astounding diversity of living organisms with remarkable abilities, such as adaptation to different environments or perturbations, and reproduction, being able to survive. Even though a lot of work has been done towards a synthesis, it is still not completely clear how to unleash the full potential of biological properties into artificial systems. This thesis tackles the problem of better understanding the developmental process between genotype and phenotype and the evolution of complex systems made of large sets of elements interacting locally and giving rise to collective behaviour. In a traditional Evolutionary Algorithm approach, the genotype maps to a phenotype directly, i.e. direct 1-to-1 encoding. If one wants to scale-up the phenotype complexity, indirect encodings, e.g. developmental or generative mappings, are a necessity. In the experimental work, the chosen computational platform is Cellular Automata (CA). The biological metaphor can be applied to the physical structure similarities between artificial cellular systems and biological multi-cellular organisms. A CA can be considered as a developing organism, where the genome specification and the gene regulation information control the growth and differentiation of the cells. Such a dynamic developmental system can show adaptation, self-modification, plasticity, and self-replication properties. In this thesis, four challenges of designing Evolutionary and Developmental (EvoDevo) systems are identified and studied further, each related to a specific research question: RQ1. What kind of information must be present in the genome in order to produce computation in any of the computational classes? RQ2. How to quantify developmental complexity, i.e. emergent phenotypic complexity? RQ3. Do genome parameters give any information on the evolvability of the system? And if yes, can genome information be used to guide evolutionary search in favourable areas of the search space where the wanted emergent behaviour is more likely to be found? RQ4. How can scalability of artificial EvoDevo systems be improved towards achieving systems that can fully unleash their inherent complexity, potentially at the levels of complexity found in nature? The results in this thesis show that abstract measures of phenotypic complexity may be suited to characterize emergent cellular organisms. Genome information may be related to emergent complexity and such knowledge may be used to guide evolutionary search. For scaled-up systems, it may be possible to allow indirect encodings with genome representation growth. A framework for the evolutionary growth of genomes is proposed.nb_NO
dc.language.isoengnb_NO
dc.publisherNTNUnb_NO
dc.relation.ispartofseriesDoctoral thesis at NTNU;2015:31
dc.relation.haspartPaper 1: Tufte, Gunnar; Nichele, Stefano. On the correlations between developmental diversity and genomic composition. I: 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011. Association for Computing Machinery (ACM) <a href="http://dx.doi.org/10.1145/2001576.2001779" target="_blank"> http://dx.doi.org/10.1145/2001576.2001779</a> ACM New York, NY, USA ©2011
dc.relation.haspartPaper 2: Nichele, Stefano; Tufte, Gunnar. Genome Parameters as Information to Forecast Emergent Developmental Behaviors. I: 11th International Conference Unconventional Computation and Natural Computation, UCNC 2012. Is not included due to copyright. Available at <a href="http://dx.doi.org/10.1007/978-3-642-32894-7_18" target="_blank"> http://dx.doi.org/10.1007/978-3-642-32894-7_18</a>
dc.relation.haspartPaper 3: Nichele, Stefano; Tufte, Gunnar. Measuring Phenotypic Structural Complexity of Artificial Cellular Organisms. Approximation of Kolmogorov Complexity with Lempel-Ziv Compression. I: Innovations in Bio-inspired Computing and Applications. Proceedings of the 4th International Conference on Innovations in Bio-inspired Computing and Applications, IBICA 2013. Is not included due to copyright. Available at <a href="http://dx.doi.org/10.1007/978-3-319-01781-5_3 " target="_blank"> http://dx.doi.org/10.1007/978-3-319-01781-5_3</a>
dc.relation.haspartPaper 4: Nichele, Stefano; Tufte, Gunnar. Evolution of Incremental Complex Behavior on Cellular Machines. I: Advances in Artificial Life, ECAL 2013.Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems © 2014 The MIT Press <a href="http://dx.doi.org/10.7551/978-0-262-31709-2-ch011 " target="_blank"> http://dx.doi.org/10.7551/978-0-262-31709-2-ch011</a>
dc.relation.haspartPaper 5: Nichele, Stefano; Wold, Håkon Hjelde; Tufte, Gunnar. Investigation of Genome Parameters and Sub-Transitions to Guide Evolution of Artificial Cellular Organisms. I: Applications of Evolutionary Computation. 17th European Conference, EvoApplications 2014 - Is not included due to copyright. Available at <a href="http://dx.doi.org/10.1007/978-3-662-45523-4_10 " target="_blank"> http://dx.doi.org/10.1007/978-3-662-45523-4_10</a>
dc.relation.haspartPaper 6: Nichele, Stefano; Tufte, Gunnar. Evolutionary Growth of Genomes for the Development and Replication of Multicellular Organisms with Indirect Encoding. I: 2014 IEEE International Conference on Evolvable Systems Proceedings - Is not available due to copyright. Available at <a href="http://dx.doi.org/10.1109/ICES.2014.7008733 " target="_blank"> http://dx.doi.org/10.1109/ICES.2014.7008733</a>
dc.relation.haspartPaper 7: S. Nichele, A. Giskeødegård and G. Tufte. Evolutionary Growth of Genome Representations on Artificial Cellular Organisms with Indirect Encodings
dc.titleEvolvability, Complexity and Scalability of Cellular Evolutionary and Developmental Systemsnb_NO
dc.typeDoctoral thesisnb_NO
dc.subject.nsiVDP::Mathematics and natural science: 400::Information and communication science: 420nb_NO


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