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dc.contributor.supervisorNolfi, Stefano
dc.contributor.authorMilano, Nicola
dc.contributor.otherFaculty of Science and Engineeringen_US
dc.date.accessioned2021-02-05T08:32:23Z
dc.date.available2021-02-05T08:32:23Z
dc.date.issued2021
dc.identifier10540606en_US
dc.identifier.urihttp://hdl.handle.net/10026.1/16857
dc.description.abstract

This thesis addresses the study of evolutionary methods to achieve robustness and evolvability in artificial systems. Chapter 1 introduces the research area, reviews the state of the art, discusses promising research directions, and presents the two major scientific objectives of the thesis. The first objective, which is covered in Chapter 2, is to verify and exploit the impact of the environmental variations on the evolvability of an artificial system. This is accomplished through the use of two experimental setup: the simulation of digital circuits and the simulation of a robotic agent situated in an external environment. Digital circuits are used to considers the variation as internal to the system, modelled as fault in circuit gates; agent-based simulation instead consider the variation in the external environment where the robot performs. The second objective, which is targeted in Chapter 3, presents the design of a new algorithm and a more efficient selection mechanism that exploits the characteristics of robustness and neutrality of the digital circuit domain. Due to its relative simplicity quantitative measures of phenotypic complexity, robustness and evolvability are obtained. Such information on the search space composition is then used to design a novel evolutionary algorithm that outperforms previously methods and to propose a selection mechanism that takes into account the phenotypic complexity of the genotypes. Chapter 4 summarizes the results obtained and describes the major contributions of the thesis.

en_US
dc.language.isoen
dc.publisherUniversity of Plymouth
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectartificial evolutionen_US
dc.subjectneural networksen_US
dc.subjectcartesian genetic programmingen_US
dc.subjectembodied agentsen_US
dc.subject.classificationPhDen_US
dc.titleOn the relation between robustness, evolvability and phenotypic complexity: Insight from Artificial Evolutionary Experimentsen_US
dc.typeThesis
plymouth.versionpublishableen_US
dc.rights.embargoperiodNo embargoen_US
dc.type.qualificationDoctorateen_US
rioxxterms.versionNA
plymouth.orcid_idhttps://orcid.org/0000-0002-1604-5161en_US


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