Skip to main content Accessibility help
×
Hostname: page-component-84b7d79bbc-l82ql Total loading time: 0 Render date: 2024-07-29T04:37:58.162Z Has data issue: false hasContentIssue false

12 - Natural computation: evolving solutions to complex problems

from Part V - Evolution and Computing

Published online by Cambridge University Press:  05 April 2012

Aldo Poiani
Affiliation:
Monash University, Victoria
Get access

Summary

Nature has evolved ways to solve many kinds of complex problems. Investigating these natural ‘solutions’ is a fruitful source of insights about the nature of complexity, and about ways to manage complex systems. Increasingly it is apparent that instead of trying to design complex systems it is often better to build systems that can evolve into robust designs. For example, evolutionary methods can produce adequate solutions to many problems of scheduling and optimisation that are intractable by traditional means. The spread of information technology throughout society has made the idea of natural computation (treating biological processes as forms of computing) increasingly influential. Evolution has inspired a host of new ideas in computing. The ideas of adaptation and evolution are crucial in emerging new computing-based technologies such as multi-agent systems, genetic regulatory networks and virtual reality.

Every age tends to see the world in terms of its preoccupations. During the Industrial Revolution, science treated the world as a great machine. Today, in the midst of an information revolution, an increasingly fruitful paradigm is to view nature as a form of computation. This new paradigm, widely known as Natural Computation, has not only provided many new insights about living systems, but has also proved to be one of the most productive and fruitful areas of computing.

Type
Chapter
Information
Pragmatic Evolution
Applications of Evolutionary Theory
, pp. 213 - 233
Publisher: Cambridge University Press
Print publication year: 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abbass, H. A. 2002 An evolutionary artificial neural networks approach for breast cancer diagnosisArtificial Intelligence in Medicine 25 265CrossRefGoogle ScholarPubMed
Albert, R.Barabasi, A. L. 2002 Statistical mechanics of complex networksReviews of Modern Physics 74 47CrossRefGoogle Scholar
Bak, P.Chen, K. 1991 Self-organized criticalityScientific American 265 26Google Scholar
Barnsley, M. 1993 Fractals EverywhereSan Diego, CA:Academic PressGoogle Scholar
Beyer, H-G.Schwefel, H -P. 2002 Evolution strategies – a comprehensive introductionNatural Computing 1 3CrossRefGoogle Scholar
Bhushan, B. 2006 Springer Handbook of NanotechnologySpringerBerlinGoogle Scholar
Buchanan, M. 2007 The best is yet to comeNature 447 39CrossRefGoogle Scholar
Chaitin, G. J. 1966 On the lengths of programs for computing binary sequencesJournal of the Association for Compuing Machinery 13 547CrossRefGoogle Scholar
Codd, E. F. 1968 Cellular Automata. ACM Monograph SeriesAcademic PressNew York, NYGoogle Scholar
Damousis, I. G.Satsios, K. J.Labridis, D. P. 2002 Combined fuzzy logic and genetic algorithm techniques – application to an electromagnetic field problemFuzzy Sets and Systems 129 371CrossRefGoogle Scholar
Dawkins, R. 1988 The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe Without DesignW. W. Norton and CompanyNew York, NYGoogle Scholar
Dejhalla, R.Mrša, Z.Vukovi, S. 2001 Application of genetic algorithm for ship hull form optimizationInternational Shipbuilding Progress 48 117Google Scholar
Fogel, D. B. 1988 An evolutionary approach to the travelling salesman problemBiological Cybernetics 60 139CrossRefGoogle Scholar
Fogel, L. J.Owens, A. J.Walsh, M. J. 1966 Artificial Intelligence through Simulated EvolutionJohn Wiley and SonsNew York, NYGoogle Scholar
Furuta, H.Maeda, K.Watanabe, E. 2008 Application of genetic algorithm to aesthetic design of bridge structuresComputer-Aided Civil and Infrastructure Engineering 10 415CrossRefGoogle Scholar
Ghasemi, J.Niazi, J.Leardi, A. 2003 Genetic-algorithm-based wavelength selection in multicomponent spectrophotometric determination by PLS: application on copper and zinc mixtureTalanta 59 311CrossRefGoogle ScholarPubMed
Gen, M.Cheng, R. 1997 Genetic Algorithms and Engineering DesignWiley-InterscienceHoboken, NJGoogle Scholar
Gondro, C.Kinghorn, B. P. 2007 A simple genetic algorithm for multiple sequence alignmentGenetics and Molecular Research 6 964Google ScholarPubMed
Green, D. G.Leishman, T. G.Sadedin, S. 2006
Guo, K. H.Zong, C. F.Kong, F. S. 2002 Objective evaluation correlated with human judgment – an approach to the optimisation of vehicle handling control systemInternational Journal of Vehicle Design 29 96CrossRefGoogle Scholar
Heng, T. N.Green, D. G. 2006 The Complexity Virtual Laboratorywww.vlab.infotech.monash.edu.auGoogle Scholar
Hill, T.Lundgren, A.Fredriksson, R. 2005 Genetic algorithm for large-scale maximum parsimony phylogenetic analysis of proteinsBiochimica et Biophysica Acta 1725 19CrossRefGoogle ScholarPubMed
Hinton, G. E.Nowlan, S. J. 1987 How learning can guide evolutionComplex Systems 1 492Google Scholar
Holland, J. 1975 Adaptation in Natural and Artificial SystemsUniversity of Michigan PressAnn Arbor, MIGoogle Scholar
Houck, C. R.Joines, J. A.Kay, M. G. 1996 Utilizing Lamarckian evolution and the Baldwin effect in hybrid genetic algorithmsNCSU-IE Technical Report96Google Scholar
Hybs, I.Gero, J. S. 1992 An evolutionary process model of designDesign Studies 13 273CrossRefGoogle Scholar
Ishibuchi, H.Kaige, S.Narukawa, K. 2005 Comparison between Lamarckian and Baldwinian repair on Multiobjective 0/1 Knapsack ProblemsCoello, C.A.C.Aguirre, A. H.Zitzler, E.Proceedings of the 3rd International Conference on Evolutionary Multi-Criterion Optimization (EMO 2005)SpringerBerlin370CrossRefGoogle Scholar
Kashtan, N.Alon, U. 2005 Spontaneous evolution of modularity and network motifsProceedings of the National Academy of Sciences 102 13773CrossRefGoogle ScholarPubMed
Kauffman, S. A. 1991 Antichaos and adaptationScientific American 265 64CrossRefGoogle ScholarPubMed
Kim, H-S.Cho, S-B. 2000 Application of interactive genetic algorithm to fashion designEngineering Applications of Artificial Intelligence 13 635CrossRefGoogle Scholar
Kirley, M.Li, X.Green, D. G. 1998 Investigation of a cellular genetic algorithm that mimics evolution in a landscapeYao, X.McKay, R.Newton, C.Kim, J.-H.Furusashi, T.SEAL98, Proceedings of the 2nd Conference on Simulated Evolution and LearningUniversity of New South WalesCanberra93Google Scholar
Kirley, M.Newth, D.Green, D. G. 2000 A tree based genetic algorithm for solving open-shop scheduling problemsInternational Journal of Knowledge-Based Intelligent Engineering Systems 4 143Google Scholar
Kolmogorov, A. N. 1965 Three approaches to the quantitative definition of informationProblems of Information Transmission 1 4Google Scholar
Koza, J. R. 1992 Genetic ProgrammingMIT PressBoston, MAGoogle Scholar
Langton, C. G. 1989 Artificial LifeAddison-WesleyReading, MA
Liu, X.Li, D.Wang, S.Tao, Z. 2007 Effective algorithm for detecting community structure in complex networks based on GA and clusteringComputational Science – ICCS 1 657Google Scholar
Loomans, M.Visser, H. 2002 Application of the genetic algorithm for optimisation of large solar hot water systemsSolar Energy 72 427CrossRefGoogle Scholar
McCormack, J. 2008 Facing the future: evolutionary possibilities for human–machine creativityMachado, P.Romero, J.The Art of Artificial Evolution: A Handbook on Evolutionary Art and MusicSpringerBerlin417CrossRefGoogle Scholar
McCormack, J.Eldridge, A.Dorin, A. 2009 Generative algorithms for making music: emergence, evolution and ecosystemsDean, R. T.The Oxford Handbook of Computer MusicOxford University PressOxford354Google Scholar
Miao, Y.Fadel, G. M.Gantovnik, V. B. 2008 Vehicle configuration design with a packing genetic algorithmInternational Journal of Heavy Vehicle Systems 15 433CrossRefGoogle Scholar
Milo, R.Shen-Orr, S.Itzkovitz, S. 2002 Network motifs: simple building blocks of complex networksScience 298 824CrossRefGoogle ScholarPubMed
Morris, G. M.Goodsell, D. S.Halliday, R. S. 1998 Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy functionJournal of Computational Chemistry 19 16393.0.CO;2-B>CrossRefGoogle Scholar
Paperin, G.Green, D. G.Leishman, T. G. 2008 Dual phase evolution and self-organisation in networksProceedings of the 7th International Conference on Simulated Evolution and Learning (SEAL'08)SpringerAmsterdam575CrossRefGoogle Scholar
Paperin, G.Sadedin, S.Green, D. G. 2011 Dual phase evolutionJournal of the Royal Society Interface 8 609CrossRefGoogle ScholarPubMed
Ray, T. S. 1991 An approach to the synthesis of lifeLangton, C.Taylor, C.Farmer, J. D.Rasmussen, S.Artificial Life II. Santa Fe Institute Studies in the Science of Complexity 11 Addison-WesleyRedwood City, CA371Google Scholar
Ripley, B. D. 2008 Pattern Recognition and Neural NetworksCambridge University PressCambridgeGoogle Scholar
Russell, S. J.Norvig, P. 2010 Artificial Intelligence: A Modern ApproachPrentice HallEnglewood Cliffs, NJGoogle Scholar
Shin, K. S.Lee, Y. J. 2002 A genetic algorithm application in bankruptcy prediction modellingExpert Systems with Applications 23 321CrossRefGoogle Scholar
Wang, C.Lefkowitz, E. J. 2005 Genomic multiple sequence alignments: refinement using a genetic algorithmBMC Bioinformatics 6 200CrossRefGoogle ScholarPubMed
Weiss, G. 2000 Multiagent Systems: A Modern Approach to Distributed Artificial IntelligenceMassachusetts Institute of TechnologyBoston, MA
Wellock, C.Ross, B. J. 2001 An examination of Lamarckian genetic algorithms2001 Genetic and Evolutionary Computation Conference (GECCO) Late Breaking Papers474http://citeseerx.ist.psu.edu/viewdoc/summary?Google Scholar
Whitley, D.Gordon, V. S.Mathias, K. 1994 Lamarckian evolution, the Baldwin effect and function optimizationDavidor, Y.Schwefel, H. -P.Männer, R.Parallel Problem-solving Methods from NatureSpringer-VerlagBerlin6Google Scholar
Witten, I. H.Frank, E. 2005 Data Mining: Practical Machine Learning Tools and TechniquesElsevierSan Francisco, CAGoogle Scholar
Wolpert, D. H.Macready, W. G. 1997 No free lunch theorems for optimizationIEEE Transactions on Evolutionary Computation 1 67CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×