eprintid: 29369 rev_number: 7 eprint_status: archive userid: 19147 dir: disk0/00/02/93/69 datestamp: 2019-02-22 14:58:25 lastmod: 2021-04-02 15:59:12 status_changed: 2019-02-22 14:58:25 type: article metadata_visibility: show creators_name: Cussat-Blanc, Sylvain creators_name: Harrington, Kyle creators_name: Pollack, Jordan creators_idrefppn: 148741762 title: Gene Regulatory Network Evolution Through Augmenting Topologies ispublished: pub subjects: subjects_INFO abstract: Artificial gene regulatory networks (GRNs) are biologically inspired dynamical systems used to control various kinds of agents, from the cells in developmental models to embodied robot swarms. Most recent work uses a genetic algorithm (GA) or an evolution strategy in order to optimize the network for a specific task. However, the empirical performances of these algorithms are unsatisfactory. This paper presents an algorithm that primarily exploits a network distance metric, which allows genetic similarity to be used for speciation and variation of GRNs. This algorithm, inspired by the successful neuroevolution of augmenting topologies algorithm's use in evolving neural networks and compositional pattern-producing networks, is based on a specific initialization method, a crossover operator based on gene alignment, and speciation based upon GRN structures. We demonstrate the effectiveness of this new algorithm by comparing our approach both to a standard GA and to evolutionary programming on four different experiments from three distinct problem domains, where the proposed algorithm excels on all experiments. date: 2015 date_type: published publisher: IEEE faculty: info divisions: IRIT keywords: Evolution - Gene regulatory networks (GRNs) - Genetic algorithm (GA) - Speciation has_fulltext: FALSE view_date_year: 2015 full_text_status: none publication: IEEE Transaction on Evolutionary Computation volume: vol. 19 number: n° 6 pagerange: 823-837 refereed: TRUE issn: 1089-778X harvester_local_overwrite: eprintid harvester_local_overwrite: userid harvester_local_overwrite: date harvester_local_overwrite: official_url harvester_local_overwrite: issn harvester_local_overwrite: dir harvester_local_overwrite: keywords harvester_local_overwrite: pagerange harvester_local_overwrite: publisher harvester_local_overwrite: volume harvester_local_overwrite: creators_name harvester_local_overwrite: faculty harvester_local_overwrite: site harvester_local_overwrite: abstract harvester_local_overwrite: title harvester_local_overwrite: publication harvester_local_overwrite: type harvester_local_overwrite: number harvester_local_overwrite: note harvester_local_overwrite: ispublished harvester_local_overwrite: id_number harvester_local_overwrite: event_title harvester_local_overwrite: pres_type harvester_local_overwrite: event_location harvester_local_overwrite: series harvester_local_overwrite: isbn harvester_local_overwrite: book_title harvester_local_overwrite: editors_name harvester_local_overwrite: department harvester_local_overwrite: thesis_type harvester_local_overwrite: pages harvester_local_overwrite: divisions harvester_local_overwrite: subjects harvester_local_overwrite: date_type harvester_local_overwrite: refereed harvester_local_overwrite: creators_idrefppn site: ut1 citation: Cussat-Blanc, Sylvain , Harrington, Kyle and Pollack, Jordan (2015) Gene Regulatory Network Evolution Through Augmenting Topologies. IEEE Transaction on Evolutionary Computation, vol. 19 (n° 6). pp. 823-837.