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A Review on Complex System Engineering

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Abstract

Complexity is commonly summarized as ‘the actions of the whole are more than the sum of the actions of the parts’. Understanding how the coherence emerges from these natural and artificial systems provides a radical shift in the process of thought, and brings huge promises for controlling and fostering this emergence. The authors define the term ‘Complex System Engineering’ to denote this approach, which aims at transferring the radical insights from Complex System Science to the pragmatic world of engineering, especially in the Computing System Engineering domain. A theoretical framework for Complex System Engineering is built by the morphogenetic engineering framework, which identifies a graduation of models, in growing order of generative power. The implementation of Complex System Engineering requires a portfolio of operational solutions: The authors therefore provide a classification of Complex System application approaches to answer this challenge and support the emergence of Complex System Engineers capable of addressing the issues of an ever more connected world.

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References

  1. Holland J, Complexity: A Very Short Introduction, Very Short Introductions, OUP Oxford, 2014.

    MATH  Google Scholar 

  2. Morin E, Introduction à la pensée complexe, Le Seuil, 2015.

    Google Scholar 

  3. Bourgine P and Lesne A, Morphogenesis: Origins of Patterns and Shapes, Springer Science & Business Media, 2010.

    Google Scholar 

  4. Zanella C, Campana M, Rizzi B, et al., Cells segmentation from 3-d confocal images of early zebrafish embryogenesis, IEEE Transactions on Image Processing, 2010, 19(3): 770–781.

    Article  MathSciNet  MATH  Google Scholar 

  5. Bogunia-Kubik K and Sugisaka M, From molecular biology to nanotechnology and nanomedicine, Biosystems, 2002, 65(2): 123–138.

    Article  Google Scholar 

  6. Simon H A, The Sciences of the Artificial, MIT Press, 1996.

    Google Scholar 

  7. Modha D S, Ananthanarayanan R, Esser S K, et al., Cognitive computing, Communications of the ACM, 2011, 54(8): 62–71.

    Article  Google Scholar 

  8. Wang Y X, Wang Y, Patel S, et al., A layered reference model of the brain (lrmb), IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2006, 36(2): 124–133, 2006.

    Article  Google Scholar 

  9. Carlson J M and Doyle J, Complexity and robustness, Proceedings of the National Academy of Sciences, 2002, 99(suppl 1): 2538–2545.

    Article  Google Scholar 

  10. van Eijnatten F, Putnik G, and Sluga A, Chaordic systems thinking for novelty in contemporary manufacturing, CIRP Annals-Manufacturing Technology, 2007, 56(1): 447–450.

    Article  Google Scholar 

  11. Doursat R, Sayama H, and Michel O, A review of morphogenetic engineering, Natural Computing, 2013, 12(4): 517–535.

    Article  MathSciNet  Google Scholar 

  12. Turing A M, Computing machinery and intelligence, Mind, 1950, 59(236): 433–460.

    Article  MathSciNet  Google Scholar 

  13. De Garis H, Shuo C, Goertzel B, et al., A world survey of artificial brain projects, part I: Largescale brain simulations, Neurocomputing, 2010, 74(1): 3–29.

    Article  Google Scholar 

  14. Goertzel B, Lian R, Arel I, et al., A world survey of artificial brain projects, part II: Biologically inspired cognitive architectures, Neurocomputing, 2010, 74(1): 30–49.

    Article  Google Scholar 

  15. Markram H, The blue brain project, Nature Reviews Neuroscience, 2006, 7(2): 153–160.

    Article  MathSciNet  Google Scholar 

  16. De Garis H, Korkin M, Gers F, et al., Building an artificial brain using an fpga based cam-brain machine, Applied Mathematics and Computation, 2000, 111(2): 163–192.

    Article  MATH  Google Scholar 

  17. Fisk D, Engineering complexity, Interdisciplinary Science Reviews, 2004, 29(2): 151–161.

    Article  Google Scholar 

  18. Fisk D and Kerherve J, Complexity as a cause of unsustainability, Ecological Complexity, 2006, 3(4): 336–343.

    Article  Google Scholar 

  19. ElMaraghy W, ElMaraghy H, Tomiyama T, et al., Complexity in engineering design and manufacturing, CIRP Annals-Manufacturing Technology, 2012, 61(2): 793–814.

    Article  Google Scholar 

  20. Carlson J M and Doyle J, Highly optimized tolerance: Robustness and design in complex systems, Physical Review Letters, 2000, 84(11): 2529.

    Article  Google Scholar 

  21. Ulieru M and Doursat R, Emergent engineering: A radical paradigm shift, International Journal of Autonomous and Adaptive Communications Systems, 2010, 4(1): 39–60.

    Article  Google Scholar 

  22. Doursat R, Organically grown architectures: Creating decentralized, autonomous systems by embryomorphic engineering, Organic Computing, Springer, 2009, 167–199.

    Google Scholar 

  23. Doursat R, Sayama H, and Michel O, Morphogenetic Engineering: Toward Programmable Complex Systems, Springer, New York, 2012.

    Book  Google Scholar 

  24. Doursat R, Programmable architectures that are complex and self-organized-from morphogenesis to engineering, ALIFE, 2008, 181–188.

    Google Scholar 

  25. Doursat R, Facilitating evolutionary innovation by developmental modularity and variability, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, ACM, 2009, 683–690.

    Google Scholar 

  26. Gorman S P, Networks, Security and Complexity: The Role of Public Policy in Critical Infrastructure Protection, Edward Elgar Publishing, 2005.

    Google Scholar 

  27. Barabási A L, The physics of the web, Physics World, 2001, 14(7): 33.

    Article  Google Scholar 

  28. Erdos P and Rényi A, Publicationes mathematicae 6, On Random Graphs, 1959, 1: 290–297.

    Google Scholar 

  29. Watts D J and Strogatz S H, Collective dynamics of “small-world’ networks, Nature, 1998, 393(6684): 440.

    Article  MATH  Google Scholar 

  30. Albert R and Barabási A L, Statistical mechanics of complex networks, Reviews of Modern Physics, 2002, 74(1): 47.

    Article  MathSciNet  MATH  Google Scholar 

  31. Barabási A L, Linked: The New Science Of Networks, 2003.

    Google Scholar 

  32. Roukny T, Bersini H, Pirotte H, et al., Default cascades in complex networks: Topology and systemic risk, Scientific Reports, 2013, 3: 2759.

    Article  Google Scholar 

  33. Carrascosa M, Eppinger S D, and Whitney D E, Using the design structure matrix to estimate product development time, Proceedings of the ASME Design Engineering Technical Conferences (Design Automation Conference), 1998, 1–10.

    Google Scholar 

  34. Eckert C M, Keller R, Earl C, et al., Supporting change processes in design: Complexity, prediction and reliability, Reliability Engineering & System Safety, 2006, 91(12): 1521–1534.

    Article  Google Scholar 

  35. Maurer M S, Structural awareness sin complex product design, PhD Thesis, Universität München, October, 2007.

    Google Scholar 

  36. Clarkson P J, Simons C, and Eckert C, Predicting change propagation in complex design, Journal of Mechanical Design (Transactions of the ASME), 2004, 126(5): 788–797.

    Article  Google Scholar 

  37. Giffin M, de Weck O, Bounova G, et al., Change propagation analysis in complex technical systems, Journal of Mechanical Design, 2009, 131(8): 081001.

    Article  Google Scholar 

  38. Pimmler T U and Eppinger S D, Integration analysis of product decompositions, ASME Design Theory and Methodology Conference, Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1994.

    Google Scholar 

  39. Browning T R, Applying the design structure matrix to system decomposition and integration problems: A review and new directions, IEEE Transactions on Engineering Management, 2001, 48(3): 292–306.

    Article  Google Scholar 

  40. Yassine A, An introduction to modeling and analyzing complex product development processes using the design structure matrix (dsm) method, Urbana, 2004, 51(9): 1–17.

    Google Scholar 

  41. Danilovic M and Sandkull B, The use of dependence structure matrix and domain mapping matrix in managing uncertainty in multiple project situations, International Journal of Project Management, 2005, 23(3): 193–203.

    Article  Google Scholar 

  42. Maurer M and Lindemann U, Structural awareness in complex product design-the multipledomain matrix, DSM 2007: Proceedings of the 9th International DSM Conference, Munich, Germany, 2007, 87–97.

    Google Scholar 

  43. Forrester J W, System dynamics, systems thinking, and soft or, System Dynamics Review, 1994, 10(2–3): 245–256.

    Article  Google Scholar 

  44. Leveson N, A new accident model for engineering safer systems, Safety Science, 2004, 42(4): 237–270.

    Article  Google Scholar 

  45. Leveson N, Daouk M, Dulac N, et al., A systems theoretic approach to safety engineering, Dept. of Aeronautics and Astronautics, Massachusetts Inst. of Technology, Cambridge, 2003.

    Google Scholar 

  46. Rasmussen J, Risk management in a dynamic society: A modelling problem, Safety Science, 1997, 27(2): 183–213.

    Article  Google Scholar 

  47. Leveson N, Dulac N, and Zipkin D, N. Dulac Engineering resilience into safety-critical systems, Resilience Engineering — Concepts and Precepts, Ashgate Aldershot, 2006, 95–123.

    Google Scholar 

  48. Dulac N, A framework for dynamic safety and risk management modeling in complex engineering systems, PhD Thesis, Citeseer, June 2007.

    Google Scholar 

  49. Barlas Y, Formal aspects of model validity and validation in system dynamics, System Dynamics Review, 1996, 12(3): 183–210.

    Article  Google Scholar 

  50. Barricelli N A, et al., Esempi numerici di processi di evoluzione, Methodos, 1954, 6(21-22): 45–68.

    MathSciNet  Google Scholar 

  51. Holland J H, Genetic algorithms and the optimal allocation of trials, SIAM Journal on Computing, 1973, 2(2): 88–105.

    Article  MathSciNet  MATH  Google Scholar 

  52. De Jong K A, Are genetic algorithms function optimizers?, PPSN, 1992, 2(1): 3–14.

    Google Scholar 

  53. Lohn J D, Linden D S, Hornby G S, et al., Evolutionary design of an x-band antenna for nasa’s space technology 5 mission, Antennas and Propagation Society International Symposium, IEEE, 2004, 3: 2313–2316.

    Article  Google Scholar 

  54. Darwin C, The Origin of Species by Means of Natural Election, Or the Preservation of Favored Races in the Struggle for Life, AL Burt., 1859.

    Google Scholar 

  55. Back T, Hammel U, and Schwefel H P, Evolutionary computation: Comments on the history and current state, IEEE Transactions on Evolutionary Computation, 1997, 1(1): 3–17.

    Article  Google Scholar 

  56. Holland J H, Genetic algorithms, Scientific American, 1992, 267(1): 66–72.

    Article  Google Scholar 

  57. Deb K, Agrawal S, Pratap A, et al., A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii, International Conference on Parallel Problem Solving From Nature, 2000, 849–858.

    Google Scholar 

  58. Goldberg D E and Holland J H, Genetic algorithms and machine learning, Machine Learning, 1988, 3(2): 95–99.

    Article  Google Scholar 

  59. Booker L B, Goldberg D E, and Holland J H, Classifier systems and genetic algorithms, Artificial Intelligence, 1989, 40(1–3): 235–282.

    Article  Google Scholar 

  60. Eigen M, Ingo rechenberg evolutionsstrategie optimierung technischer systeme nach prinzipien der biologishen evolution, mit einem Nachwort von Manfred Eigen, Friedrich Frommann Verlag, Struttgart-Bad Cannstatt, 1973, 45: 46–47.

    Google Scholar 

  61. Schwefel H P, Numerische Optimierung von Computer-Modellen Mittels der Evolutionsstrategie, Birkhäuser, Basel Switzerland, 1977

    Book  MATH  Google Scholar 

  62. Schwefel H P, Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution, Annals of Operations Research, 1984, 1(2): 165–167.

    Article  Google Scholar 

  63. Bäck T and Schwefel H P, An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation, 1993, 1(1): 1–23.

    Article  Google Scholar 

  64. Koza J R, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, 1992.

    MATH  Google Scholar 

  65. Fogel L J, Owens A J, and Walsh M J, Artificial Intelligence Through Simulated Evolution, John Wiley, 1966.

    MATH  Google Scholar 

  66. Fogel L J, Evolutionary programming in perspective: The top-down view, Computational Intelligence: Imitating Life, 1994.

    Google Scholar 

  67. Moscato P, et al., On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms, Caltech Concurrent Computation Program, C3P Report, 1989, 826: 1989.

    Google Scholar 

  68. Storn R and Price K, Differential evolution — A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 1997, 11(4): 341–359.

    Article  MathSciNet  MATH  Google Scholar 

  69. Zaharie D and Micota F, Revisiting the analysis of population variance in differential evolution algorithms, IEEE Congress Eonvolutionary Computation (CEC), 2017, 1811–1818.

    Google Scholar 

  70. Fonseca C M and Fleming P J, An overview of evolutionary algorithms in multiobjective optimization, Evolutionary Computation, 1995, 3(1): 1–16.

    Article  Google Scholar 

  71. Zitzler E and Thiele L, Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach, IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257–271.

    Article  Google Scholar 

  72. Deb K, Pratap A, Agarwal S, et al., A fast and elitist multiobjective genetic algorithm: Nsga-ii, IEEE transactions on Evolutionary Computation, 2002, 6(2): 182–197.

    Article  Google Scholar 

  73. Deb K and Jain H, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints, IEEE Trans. Evolutionary Computation, 2014, 18(4): 577–601.

    Article  Google Scholar 

  74. Sharma D and Collet P, An archived-based stochastic ranking evolutionary algorithm (asrea) for multi-objective optimization, Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, 2010, 479–486.

    Chapter  Google Scholar 

  75. Knowles J D and Corne D W, Approximating the nondominated front using the pareto archived evolution strategy, Evolutionary Computation, 2000, 8(2): 149–172.

    Article  Google Scholar 

  76. Collet P and Schoenauer M, Guide: Unifying evolutionary engines through a graphical user interface, International Conference on Artificial Evolution (Evolution Artificielle), Springer, 2003, 203–215.

    Google Scholar 

  77. Eberhart R and Kennedy J, A new optimizer using particle swarm theory, IEEE Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, 39–43.

    Chapter  Google Scholar 

  78. Geem Z W, Kim J H, and Loganathan G, A new heuristic optimization algorithm: Harmony search, Simulation, 2001, 76(2): 60–68.

    Article  Google Scholar 

  79. Holland J H, Adaptation in natural and artificial systems: An introductory analysis with application to biology, control, and artificial intelligence, Ann Arbor, MI: University of Michigan Press, 1975.

    MATH  Google Scholar 

  80. Dejong K, An analysis of the behaviour of a class of genetic adaptive systems, PhD Thesis, Dept. of Computer and Communication Sciences, University of Michigan, Ann Arbor, 1975.

    Google Scholar 

  81. Bull L, Learning classifier systems: A brief introduction, Applications of Learning Classifier Systems, 2004, 1–12.

    Chapter  MATH  Google Scholar 

  82. Smith S F, Flexible learning of problem solving heuristics through adaptive search, IJCAI, 1983, 83: 422–425.

    Google Scholar 

  83. Bacardit J and Garrell J M, Evolving multiple discretizations with adaptive intervals for a pittsburgh rule-based learning classifier system, Genetic and Evolutionary Computation Conference, 2003, 1818–1831.

    Google Scholar 

  84. Goldberg D E, Computer-aided gas pipeline operation using genetic algorithms and rule learning, PhD Thesis, University of Michigan, January, 1983.

    Google Scholar 

  85. Holland J H, Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems, Machine Learning: An Arti-Ficial Intelligence Approach, 1986, 593–623.

    Google Scholar 

  86. Collet P, Lutton E, Raynal F, et al., Polar IFS+ Individual genetic programming = efficient IFS inverse problem solving, Genetic Programming and Evolvable Machines, 2000, 1(4): 339–361.

    Article  MATH  Google Scholar 

  87. Hutchinson J E, Fractals and self similarity, Indiana University Mathematics Journal, 1981, 30(5): 713–747.

    Article  MathSciNet  MATH  Google Scholar 

  88. Èrepinšek M, Liu S H, and Mernik M, Exploration and exploitation in evolutionary algorithms: A survey, ACM Computing Surveys (CSUR), 2013, 45(3): 35.

    Google Scholar 

  89. Martin W, Lienig J, and Cohoon J P, C6. 3 island (migration) models: Evolutionary algorithms based on punctuated equilibria, Seiten C, 1997s.

    Google Scholar 

  90. Melab N, Talbi E G, et al., Gpu-based island model for evolutionary algorithms, Proceedings of the 12th annual conference on Genetic and Evolutionary Computation, ACM, 2010, 1089–1096.

    Google Scholar 

  91. Arenas M G, Collet P, Eiben A E, et al., A framework for distributed evolutionary algorithms, International Conference on Parallel Problem Solving from Nature, 2002, 665–675.

    Google Scholar 

  92. Maitre O, Baumes L A, Lachiche N, et al., Coarse grain parallelization of evolutionary algorithms on gpgpu cards with easea, Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, 2009, 1403–1410.

    Chapter  Google Scholar 

  93. Krüger F, Baumes L, and Collet P, Exploiting clusters of gpu machines with the easea platform, Artificial Evolution 2011 (Evolution Artificielle 2011), 2011.

    Google Scholar 

  94. Dorigo M, Maniezzo V, and Colorni A, Ant system: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996, 26(1): 29–41.

    Article  Google Scholar 

  95. Deneubourg J L and Goss S, Collective patterns and decision-making, Ethology Ecology & Evolution, 1989, 1(4): 295–311.

    Article  Google Scholar 

  96. Deneubourg J L, Aron S, Goss S, et al., The self-organizing exploratory pattern of the argentine ant, Journal of Insect Behavior, 1990, 3(2): 159–168, 1990.

    Article  Google Scholar 

  97. Goss S, Aron S, Deneubourg J L, et al., Self-organized shortcuts in the argentine ant, Naturwissenschaften, 1989, 76(12): 579–581.

    Article  Google Scholar 

  98. Louchet J, Guyon M, Lesot M J, et al., Dynamic flies: A new pattern recognition tool applied to stereo sequence processing, Pattern Recognition Letters, 2002, 23(1): 335–345.

    Article  MATH  Google Scholar 

  99. Langdon W B, Genetic improvement of programs, 2014 16th IEEE International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2014, 14–19.

    Chapter  Google Scholar 

  100. Salustowicz R and Schmidhuber J, Probabilistic incremental program evolution, Evolutionary Computation, 1997, 5(2): 123–141.

    Article  Google Scholar 

  101. Haraldsson S O, Woodward J R, Brownlee A E, et al., Exploring fitness and edit distance of mutated python programs, European Conference on Genetic Programming, 2017, 19–34.

    Chapter  Google Scholar 

  102. Langdon W B and Petke J, Software is not fragile, First Complex Systems Digital Campus World E-Conference 2015, 2017, 203–211.

    Chapter  Google Scholar 

  103. Le Goues C, Nguyen T, Forrest S, et al., Genprog: A generic method for automatic software repair, IEEE Transactions on Software Engineering, 2012, 38(1): 54–72.

    Article  Google Scholar 

  104. Schulte E M, Weimer W, and Forrest S, Repairing cots router firmware without access to source code or test suites: A case study in evolutionary software repair, Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, ACM, 2015, 847–854.

    Chapter  Google Scholar 

  105. Błądek I and Krawiec K, Evolutionary program sketching, European Conference on Genetic Programming, Springer, New York, 2017, 3–18.

    Chapter  Google Scholar 

  106. Ranise S and Tinelli C, Satisfiability modulo theories, Trends and Controversies-IEEE Intelligent Systems Magazine, 2006, 21(6): 71–81.

    Google Scholar 

  107. Barrett C W, Sebastiani R, Seshia S A, et al., Satisfiability modulo theories, Handbook of Satisfiability, 2009, 185: 825–885.

    Google Scholar 

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Acknowledgements

We thank the CSTB team at ICube laboratory, René Doursat from the Manchester University for valuable exchanges on the subject of morphogenetic engineering and Claudia Eckert from the Open University in London for her pedagogical work on Design Structure Matrices.

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Correspondence to Pierre Parrend or Pierre Collet.

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This paper was recommended for publication by Editor DI Zengru.

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Parrend, P., Collet, P. A Review on Complex System Engineering. J Syst Sci Complex 33, 1755–1784 (2020). https://doi.org/10.1007/s11424-020-8275-0

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