Rob Mills

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Richard A. WatsonUniversity of SouthamptonVerified email at ecs.soton.ac.uk
Christopher L. BuckleyUniversity of SussexVerified email at sussex.ac.uk
Thomas SchmicklInstitute of Biology, Artificial Life Lab, University of GrazVerified email at thomasschmickl.science
Simon T. PowersLecturer, School of Computing, Edinburgh Napier UniversityVerified email at napier.ac.uk
Luís CorreiaLASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de LisboaVerified email at ciencias.ulisboa.pt
Francesco MondadaEcole Polytechnique Fédérale de LausanneVerified email at epfl.ch
Martina SzopekUniversity of GrazVerified email at uni-graz.at
Pedro MarianoISCTE, Instituto Universitário de Lisboa / LASIGE, Universidade de LisboaVerified email at iscte-iul.pt
Jason NobleRetired from academic work. (Formerly at University of Southampton.)Verified email at ecs.soton.ac.uk
frank bonnetBionomousVerified email at epfl.ch
Alexandra PennCentre for the Evaluation of Complexity Across the Nexus (CECAN), University of SurreyVerified email at surrey.ac.uk
Gunter WagnerProfessor Emeritus of Ecology and Evolutionary Biology, Yale UniversityVerified email at yale.edu
Daniel M WeinreichBrown UniversityVerified email at brown.edu
Martin StefanecPhD student in Biology, University of GrazVerified email at uni-graz.at
Loizos KouniosVerified email at ecs.soton.ac.uk
Eörs Szathmáry, research professorCentre for Ecological ResearchVerified email at ecolres.hu
Daniel Nicolas HofstadlerUniversity of GrazVerified email at uni-graz.at
Thomas JansenAberystwyth UniversityVerified email at aber.ac.uk
Seth BullockToshiba Chair in Data Science and Simulation, University of BristolVerified email at bristol.ac.uk

Rob Mills

EPFL

Verified email at epfl.ch

complex biological systems evolutionary simulation modelling collective behaviour natural and artificial intelligence


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The Evolution of Phenotypic Correlations and “Developmental Memory” RA Watson, GP Wagner, M Pavlicev, DM Weinreich, R Mills Evolution 68 (4), 1124-1138, 2014	111	2014
Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions RA Watson, R Mills, CL Buckley, K Kouvaris, A Jackson, ST Powers, ... Evolutionary Biology 43 (4), 553-581, 2016	69	2016
Optimization in “self‐modeling” complex adaptive systems RA Watson, CL Buckley, R Mills Complexity 16 (5), 17-26, 2011	60	2011
Global adaptation in networks of selfish components: Emergent associative memory at the system scale RA Watson, R Mills, CL Buckley Artificial Life 17 (3), 147-166, 2011	54	2011
What can ecosystems learn? Expanding evolutionary ecology with learning theory DA Power, RA Watson, E Szathmáry, R Mills, ST Powers, CP Doncaster, ... Biology direct 10 (1), 69, 2015	51	2015
Associative memory in gene regulation networks RA Watson, CL Buckley, R Mills, AP Davies Proceedings of the Twelfth International Conference on the Synthesis and …, 2010	48	2010
Robots mediating interactions between animals for interspecies collective behaviors F Bonnet, R Mills, M Szopek, S Schönwetter-Fuchs, J Halloy, S Bogdan, ... Science Robotics 4 (28), eaau7897, 2019	47	2019
On crossing fitness valleys with the Baldwin effect R Mills, RA Watson Proceedings of the Tenth International Conference on the Simulation and …, 2006	38	2006
“If You Can't Be With the One You Love, Love the One You're With”: How Individual Habituation of Agent Interactions Improves Global Utility AP Davies, RA Watson, R Mills, CL Buckley, J Noble Artificial life 17 (3), 167-181, 2011	30	2011
Can selfish symbioses effect higher-level selection? RA Watson, N Palmius, R Mills, ST Powers, A Penn Advances in artificial life. Darwin Meets von Neumann, 27-36, 2009	30	2009
Transformations in the scale of behavior and the global optimization of constraints in adaptive networks RA Watson, R Mills, CL Buckley Adaptive Behavior 19 (4), 227-249, 2011	28	2011
Symbiosis enables the evolution of rare complexes in structured environments R Mills, RA Watson Advances in Artificial Life. Darwin Meets von Neumann, 110-117, 2009	19	2009
The effect of Hebbian learning on optimisation in Hopfield networks RA Watson, CL Buckley, R Mills University of Southampton, 2009	19	2009
Transforming Evolutionary Search Into Higher-Level Evolutionary Search by Capturing Problem Structure R Mills, T Jansen, R Watson IEEE Transactions on Evolutionary Computation 18 (5), 628--642, 2014	18	2014
Governing the swarm: Controlling a bio-hybrid society of bees & robots with computational feedback loops M Stefanec, M Szopek, T Schmickl, R Mills 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2017	16	2017
How micro-evolution can guide macro-evolution: Multi-scale search via evolved modular variation R Mills University of Southampton, 2010	16	2010
Social environment construction provides an adaptive explanation for new levels of individuality ST Powers, R Mills, AS Penn, RA Watson Proceedings of Workshop on Levels of Selection and Individuality in …, 2009	16*	2009
Symbiosis, synergy and modularity: introducing the reciprocal synergy symbiosis algorithm R Mills, R Watson Advances in Artificial Life, 1192-1201, 2007	14	2007
Genetic assimilation and canalisation in the Baldwin effect R Mills, R Watson Advances in Artificial Life, 353-362, 2005	14	2005
Coordination of collective behaviours in spatially separated agents R Mills, P Zahadat, F Silva, D Mlikic, P Mariano, T Schmickl, L Correia Proceedings of the European Conference on Artificial Life 2015, 579--586, 2015	13	2015

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