EvoMachina is a novel natural computation algorithm, inspired by recent understandings of the processes of genome reorganisation in bacteria and viruses. It has been developed as part of the EU FP7 project EvoEvo, taking inspiration from its biological experiments, and developed to support Living Technology applications. This abstract outlines the conceptual model underlying EvoMachina, its implementation, and a reference application

Hoverd, Tim

Stepney, Susan

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This is a repository copy of EvoMachina : a novel evolutionary algorithm inspired by bacterial genome reorganisation.White Rose Research Online URL for this paper:https://eprints.whiterose.ac.uk/113742/Version: Published VersionConference or Workshop Item:Hoverd, Tim and Stepney, Susan orcid.org/0000-0003-3146-5401 (2016) EvoMachina : a novel evolutionary algorithm inspired by bacterial genome reorganisation. In: 2nd EvoEvo Workshop, CCS 2016, 20 Sep 2016. eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. 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EvoMachina: a novel evolutionary algorithminspired by bacterial genome reorganisationTim Hoverd and Susan StepneyDepartment of Computer Science, University of York, UKYork Centre for Complex Systems Analysis, University of York, UKtim.hoverd@york.ac.uk,susan.stepney@york.ac.ukIntroductionEvoMachina is a novel natural computation algorithm, inspired by recent un-derstandings of the processes of genome reorganisation in bacteria and viruses.It has been developed as part of the EU FP7 project EvoEvo, taking inspirationfrom its biological experiments, and developed to support Living Technologyapplications.This abstract outlines the conceptual model underlying EvoMachina, its im-plementation, and a reference application.The conceptual modelThe basics concepts of EvoMachina are Structures (Machines, Templates, Repos-itories), Domains, and Spaces (individuals and Sites) [1], see figure 1.– Structure. A structure is a sequence of objects of a particular type, thatstores information, and may additionally have behaviour.• Machine. A machine is an active structure; different machines performthe various operations in the system, such as mutation, replication, ex-pression, and domain-specific activities. It is the analogue of the proteinin a cell. Machines may degrade, and so need continual replenishment.• Template. Machines are described by Templates. Translator machinesbuild a specific machine from its template description. A template is theanalogue of mRNA in cells.• Repository. Templates are stored in a Repository. Transcriber ma-chines extract individual templates from the repository. Kloner machinesbuild mutated repositories on replication. Any information incorporatedin associated machines can therefore evolve. The repository is the ana-logue of a DNA chromosome in cells.– Domain. A domain captures the ‘physics’ of a particular collection of struc-tures. There may be multiple domains, in particular, domains related toevolution, and domains related to problem-specific features.2 Tim Hoverd and Susan StepneyFig. 1. Conceptual model of EvoMachina: see text for details.– Space.• Individual. An individual is a space that contains a collection of Struc-tures, comprising various machines, templates, and repositories. Its be-haviour is given by the activity of its various machines. It is the analogueof a cell. Individuals may contain other individuals, analogues of cellswith compartments.• Site. A site is a space that can contain individuals; it is the implemen-tation of physical location. A site may also contain others sites, allowinghierarchical spatial structures.An Individual is a candidate solution. Individuals replicate when some criterionis met, such as a clock tick and winning a tournament for generational algo-rithms, or a suitable energy level or machine concentration being reached inother approaches.ImplementationThe conceptual model outlined above has been translated into a ‘Platform model’[2, 7], making design decisions suitable for implementation in a concurrent object-oriented language.The EvoMachina framework [5] is implemented in Java8, using its concur-rency management to allow machines and spaces to execute concurrently, andexploiting lambda expressions for succinct customisation of application-specificmachines and domains.EvoMachina 3Fig. 2. Instantiation of an EvoMachina Individual for the TSP: see text for details.(Some machines are omitted for clarity.)Replication uses a specific machine, the Kloner, which copies the repositorieswith mutation (including genome reorganisation operations). Relevant machinesare cloned or shared, depending on the particular parameter settings. This repli-cation, although executed by machines, is not itself an implementation of abiological process; rather it is a shortcut mechanism [3], allowing the algorithmto focus on the relevant biological concepts.The tournaments, or other processes that trigger replication, are mediatedby the sites, through Environment Orientation [6].Example: Travelling Salesperson Problem (TSP)A reference implementation of TSP is provided with the EvoMachina framework.It comprises three domains with the following structures (figure 2):– TSP Domain• The domain encodes the cities, the distances between each pair, and thecalculation of fitness in terms of journey length.• The TSP repository comprises a single template, a particular permutedlist of cities, along with some non-coding items.• The (hard-coded) TSP Transcriber machine builds a TSP template fromthe TSP repository. This template comprises the permuted list of cities4 Tim Hoverd and Susan Stepneywith the non-coding items removed, which is a direct encoding of thecandidate solution.• The (hard-coded) application-specific TSP fitness calculator machine isused by the domain to calculate the fitness (path-length) of a candidatesolution template.– k-opt Domain• The k-opt domain encodes details of k-opt mutation operations on TSPrepository permutations.• The k-opt repository is a list of integers, comprising the available valuesof k for k-opt mutations.• The (hard-coded) k-opt Transcriber machine builds a k-opt templatefrom the k-opt repository. The k-opt template is a single integer value.• The (hard-coded) k-opt Translator machine builds a TSP Kloner ma-chine from a k-opt template, embedding the specific value of k alongwith some hard-coded behaviour.• The TSP Kloner machine is used in the making of a copy of its individual;it uses its specific value of k built in by the Translator machine to mutatethe TSP repository, building a new candidate solution.– k-opt Kloner Domain• The k-opt kloner domain encodes details of mutation operations on k-optrepositories.• The (hard-wired) k-opt kloner machine is used in the making of a copy ofits individual; to copy and mutate the k-opt Repository in a way definedin the k-opt Kloner Domain, changing the k values available in the newindividual.An Individual initially comprises a TSP Repository, a k-opt Repository, andthe relevant Transcriber and Translator Machines. These express the particularKloners when needed.The overall Space can either be a single site, containing multiple individuals,corresponding to a well-mixed system, or it can be an n-D toroidal lattice ofsites, for a spatially varying system, with mobile individuals.When run on 48 USA State capitals data, the fittest individuals tend to theshortest pathlength (the optimum value of 33551). See figure 3.SummaryThe relatively complex architecture of EvoMachina incorporates many of theconcepts of modern biological evolutionary knowledge: genome reordering, trans-lation and transcription expression, evolving mutability, metabolic networks(through further machines encoded in repositories, or hard-wired, not coveredhere), and more. The aim is to develop a system that can evolve its evolvability,to adapt to online data streams, and support an open-ended reflective evolution-ary system [4].EvoMachina 5Fig. 3. Results from a typical run of EvoMachina on the 48 US state capitals TSP: seetext for details. Each dot represents the birth of an individual; the colour representsthe k-value of the parent used to create this individual: darker colours represent lowerk values. (top) fitness plotted against time of birth; (bottom) k-value of parent plottedagainst time of birth.6 Tim Hoverd and Susan StepneyAcknowledgements. This work is funded by the EU FP7 project EvoEvo, grantnumber 610427.References1. Paul Andrews and Susan Stepney. A metamodel for the evolution of evolution. InECAL 2015, York, UK, July 2015, pages 621–628. MIT Press, 2015.2. Paul S. Andrews, Susan Stepney, and Jon Timmis. Simulation as a scientific in-strument. In Proceedings of the 2012 Workshop on Complex Systems Modelling andSimulation, Orleans, France, September 2012, pages 1–10. Luniver Press, 2012.3. Wolfgang Banzhaf, Bert Baumgaertner, Guillaume Beslon, Ren Doursat, James A.Foster, Barry McMullin, Vinicius Veloso de Melo, Thomas Miconi, Lee Spec-tor, Susan Stepney, and Roger White. Defining and simulating open-ended nov-elty: Requirements, guidelines, and challenges. Theory in Biosciences, 2016.doi:10.1007/s12064-016-0229-7.4. Simon Hickinbotham and Susan Stepney. Bio-reflective architectures. In ALife2016, Cancun, Mexico, July 2016. MIT Press, 2016.5. Tim Hoverd. EvoMachina user documentation. EvoEvo deliverable 4.3, Departmentof Computer Science, University of York, 2016. Available with EvoMachina softwarefrom github/evoevo-york/evomachina.6. Tim Hoverd and Susan Stepney. Environment orientation: a structured simulationapproach for agent-based complex system. Natural Computing, 14(1):83–97, 2015.7. Susan Stepney and Paul S. Andrews. CoSMoS special issue editorial. NaturalComputing, 14(1):1–6, 2015.

EvoMachina:a novel evolutionary algorithm inspired by bacterial genome reorganisation

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EvoMachina : a novel evolutionary algorithm inspired by bacterial genome reorganisation

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