Table Of ContentEmergence, Complexity and Computation ECC
Larry Bull
The Evolution
of Complexity
Simple Simulations of Major Innovations
Emergence, Complexity and Computation
Volume 37
Series Editors
Ivan Zelinka, Technical University of Ostrava, Ostrava, Czech Republic
Andrew Adamatzky, University of the West of England, Bristol, UK
Guanrong Chen, City University of Hong Kong, Hong Kong, China
Editorial Board
Ajith Abraham, MirLabs, USA
AnaLucia,UniversidadeFederaldoRioGrandedoSul,PortoAlegre,RioGrande
do Sul, Brazil
Juan C. Burguillo, University of Vigo, Spain
Sergej Čelikovský, Academy of Sciences of the Czech Republic, Czech Republic
Mohammed Chadli, University of Jules Verne, France
Emilio Corchado, University of Salamanca, Spain
Donald Davendra, Technical University of Ostrava, Czech Republic
Andrew Ilachinski, Center for Naval Analyses, USA
Jouni Lampinen, University of Vaasa, Finland
Martin Middendorf, University of Leipzig, Germany
Edward Ott, University of Maryland, USA
Linqiang Pan, Huazhong University of Science and Technology, Wuhan, China
Gheorghe Păun, Romanian Academy, Bucharest, Romania
Hendrik Richter, HTWK Leipzig University of Applied Sciences, Germany
Juan A. Rodriguez-Aguilar , IIIA-CSIC, Spain
Otto Rössler, Institute of Physical and Theoretical Chemistry, Tübingen, Germany
Vaclav Snasel, Technical University of Ostrava, Czech Republic
Ivo Vondrák, Technical University of Ostrava, Czech Republic
Hector Zenil, Karolinska Institute, Sweden
The Emergence, Complexity and Computation (ECC) series publishes new
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Lloyd limits…) as well as algorithmic limits (Gödel’s proof and its impact on
calculation,algorithmiccomplexity,theChaitin’sOmeganumberandKolmogorov
complexity, non-traditional calculations like Turing machine process and its
consequences,…) and limitations arising in artificial intelligence. The topics are
(but not limited to) membrane computing, DNA computing, immune computing,
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computing on the edge of chaos, computational aspects of dynamics of complex
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coming from mutual intersection of classical as well as modern methods of
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contributions from specialized conferences and workshops, special contribution
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Larry Bull
The Evolution of Complexity
Simple Simulations of Major Innovations
123
Larry Bull
University of the West of England
Department ofComputer Science
andCreative Technologies
Bristol, UK
ISSN 2194-7287 ISSN 2194-7295 (electronic)
Emergence, Complexity andComputation
ISBN978-3-030-40729-2 ISBN978-3-030-40730-8 (eBook)
https://doi.org/10.1007/978-3-030-40730-8
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For my girls.
Preface
As a computer scientist who has spent nearly thirty years drawing upon the natural
worldforinspirationtomakemachinesdousefulthings,myresearchwithevolution
has been somewhat separated from squishy biology. Instead of DNA, I have used
evolutionwithdataand/orinstructionsforcomputerstodesignsuchthingsasmodels
of Olympic athletes, arrays of interacting wind turbines, nanoparticles for cancer
tumourtreatment,computersmadefromchemicalreactions,etc.Thatis,evolutionas
apowerfulsearchtoolbywhichtonegotiatecomplexity.Alongsidethatwork,Ihave
always turned such abstracted evolution back onto the natural phenomena to help
gain insight of the underlying dynamics and emergence/benefits of each. This book
brings together much of that work—both old and new—to explore a number of the
key increases in complexity seen in the natural world. Whilst any increase in com-
plexity has certainly not been inevitable, they have occurred and this book seeks to
explain each of them purely in terms of the features offitness landscapes.
Ihavebeenfortunateenoughtodiscussthisworkwithsomanypeopleoverthe
manyyearsthattoattempttonamethemallherewouldbefolly.ButIwouldliketo
thank the Editors for publishing me in their series.
Bristol, UK Larry Bull
vii
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Evolutionary Innovations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 The Baldwin Effect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Genomes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 The NK Model: Asexual Haploid Evolution. . . . . . . . . . . . . . . . . 5
2.2 Genome Growth in the NK Model . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3 Symbiosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1 The NKCS Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 Endosymbiosis in the NKCS Model . . . . . . . . . . . . . . . . . . . . . . 20
3.3 Horizontal Gene Transfer in Hereditary Endosymbiosis . . . . . . . . 25
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4 Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.1 The Baldwin Effect in the NK Model . . . . . . . . . . . . . . . . . . . . . 32
4.2 Evolution of the Haploid-Diploid Cycle: The Baldwin Effect . . . . 33
4.3 Two-Step Meiosis and Recombination: Altering the Amount
of Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.4 Genome Growth in Sexual Diploids . . . . . . . . . . . . . . . . . . . . . . 40
4.5 Coevolving Sexual Diploids . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
ix
x Contents
5 Chromosomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.1 Chromosome Number. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.2 Sex Chromosomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.3 Dominance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6 Multicellularity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.1 Multicellularity in the NK Model . . . . . . . . . . . . . . . . . . . . . . . . 63
6.2 Functional Differentiation and Simple Epigenetic Control . . . . . . . 66
6.3 Eusociality: Haplodiploid Multicellularity. . . . . . . . . . . . . . . . . . . 68
6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Appendix: Regulation... .... ..... .... .... .... .... .... ..... .... 75
Index .... .... .... .... .... ..... .... .... .... .... .... ..... .... 87
Chapter 1
Introduction
Complexityishardtodefineortomeasure,butthereissurelysomesenseinwhichelephants
andoaktreesaremorecomplexthanbacteria,andbacteriathanthefirstreplicatingmolecules.
[7,p.3]
Lifeonearthemergedaround4billionyearsagoanditscomplexityhasbeenincreas-
ingeversince.Thisbookseekstoexploretheconditionsunderwhichnaturalselection
[3]wouldfavoursomeofthekeymechanismsbywhichthoseincreasesincomplex-
ityhavecomeabout,usingsimplemodelsofevolutiononabstractfitnesslandscapes.
Wright[11]wasperhapsthefirsttoviewnaturalevolutionasaprocessofadaptation
throughamultidimensionalspaceoffitnesspeaksandtroughs(Fig.1.1,top).Turing
[10]wouldlaterhighlightthepotentialuniversalityofthatviewwhenconsidering
waystodesignintelligentcomputers:evolutionasageneralsearchprocess.Whilst
nosimplecorrelationbetweentheamountofDNAinagivenorganismanditsper-
ceived complexity exists—lilies have more DNA than humans, for example—it is
clearthatthetwoareinterrelated.Fivewaysinwhichanincreaseintheamountof
DNAmayoccurareexploredhere.
1.1 EvolutionaryInnovations
Thefollowingsourcesofevolutionaryinnovationareconsidered:
Genomes Once formed, increases inthe amount of rawmaterial available
toevolutioninthegenomeisonewaythroughwhichnewfunc-
tionalitiesmayemerge,potentiallyresultinginanovelproteinor
newregulatorycontrol.
Symbiosis Thebringingtogetherofcloselyinteractingorganismssuchthata
newleveloffunctionalityisrealisedbyoneormoreofthepartners
isubiquitousandwaskeytotheevolutionofeukaryotesaround
1.8billionyearsago.
©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringer 1
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