Automated replication optimization for protocellular information system

Ditlev Hartmann Bornebusch, Christina Colaluca Sørensen, Peter Zingg, Gianluca Gazzola, Norman H. Packard, Steen Rasmussen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

Due to the high cost of experiments, high dimensional complex systems with multiple parameters usually pose grand challenges not only in artificial life but in areas including manufacturing processes, supply chains as well as services in the healthcare sector. We present and verify a fully automated method of reducing the needed experiments to identify optimal operational conditions for complex systems, here tested in simulation on a protocellular information system. The method iteratively becomes better at locating system optima through an adaptive data analysis, which is an advantage over eg a Monte Carlo optimization method (2).
Original languageEnglish
Title of host publicationArtificial Life Conference Proceedings
PublisherMIT Press
Publication date1. Jul 2020
Pages219-220
DOIs
Publication statusPublished - 1. Jul 2020
EventALIFE 2020: The 2020 Conference on Artificial Life - Virtual
Duration: 13. Jul 202018. Jul 2020

Conference

ConferenceALIFE 2020: The 2020 Conference on Artificial Life
CityVirtual
Period13/07/202018/07/2020

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