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Robustness, evolvability and phenotypic complexity: insights from evolving digital circuits

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Abstract

We analyze the relation between robustness to mutations, phenotypic complexity, and evolvability in the context of artificial circuits evolved for the ability to solve a parity problem. We demonstrate that whether robustness to mutations enhances or diminishes phenotypic variability and evolvability depends on whether robustness is achieved through the development of parsimonious (phenotypically simple) solutions, that minimize the number of genes playing functional roles, or through phenotypically more complex solutions, capable of buffering the effect of mutations. We show that the characteristics of the selection process strongly influence the robustness and the performance of the evolving candidate solutions. Finally, we propose a new evolutionary method that outperforms evolutionary algorithms commonly used in this domain.

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References

  • Abdelhalim L, Blachon S, Selbig J, Nikolosky Z (2011) Robustness of metabolic networks: a review of existing definitions. Biosystems 106(1):1–8

    Article  Google Scholar 

  • Adami C (2002) Sequence complexity in Darwinian evolution. Complexity 8:49–57

    Article  Google Scholar 

  • Ancel LW, Fontana W (2000) Plasticity, evolvability, and modularity in RNA. J Exp Zool Part B Mol Dev Evol 288:242–283

    Article  Google Scholar 

  • Back T (1994) Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. In: Proceedings of 1st IEEE conference evolutionary computation, Jun. 27–29, 1994, pp 57–62

  • Bäck T, Hammel U (1994) Evolution strategies applied to perturbed objective functions. In Proceedings of the international conference on evolutionary computation. pp 40–45

  • Balch M (2003) Complete digital design. McGraw-Hill, New York

    Google Scholar 

  • Bedau MA, Packard NH (2003) Evolution of evolvability via adaptation of mutation rates. Biosystems 69:143–162

    Article  Google Scholar 

  • Beyer HG, Schwefel HP (2002) Evolution strategies—a comprehensive introduction. Nat Comput 1(1):3–52

    Article  MathSciNet  MATH  Google Scholar 

  • Carlson JM, Doyle J (2002) Complexity and robustness. PNAS 99:2538–2545

    Article  Google Scholar 

  • Crutchfield JP, Görnerup O (2006) Objects that make objects: the population dynamics of structural complexity. J R Soc Interface 3:345–349

    Article  Google Scholar 

  • De Visser JA et al (2003) Perspective: evolution and detection of genetic robustness. Evolution 57(9):1959–1972

    Google Scholar 

  • Earl DJ, Deem MW (2004) Evolvability is a selectable trait. PNAS 101:11531–11536

    Article  Google Scholar 

  • Edelman GM, Gally JA (2001) Degeneracy and complexity in biological systems. Proc Natl Acad Sci USA, 98(13):763–768

    Google Scholar 

  • Frei R, Whitacre J (2012) Degeneracy and networked buffering: principles for supporting emergent evolvability in agile manufacturing systems. Nat Comput 11(3):417–430

    Article  MathSciNet  Google Scholar 

  • Hartmann M, Haddow P (2004) Evolution of fault tolerant and noise-robust digital designs. IEE Proc Comput Digit Tech 151:287–294

    Article  Google Scholar 

  • Hazen RM, Griffin PL, Carothers JM, Szostak JW (2007) Functional information and the emergence of biocomplexity. Proc Natl Acad Sci 104:8574–8581

    Article  Google Scholar 

  • Houle D (1992) Comparing evolvability and variability of quantitative traits. Genetics 130:195–204

    Google Scholar 

  • Hu T, Payne JL, Banzhaf W, Moore JH (2012) Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming. Genet Program Evolvable Mach 13(3):305–337

    Article  Google Scholar 

  • Jin Y, Branke K (2005) Evolutionary optimization in uncertain environments: a survey. IEEE Trans Evol Comput 9(3):303–317

    Article  Google Scholar 

  • Kirschner M, Gerhart J (1998) Evolvability PNAS 95:8420–8427

    Article  Google Scholar 

  • Levitan B, Kauffman S (1994) Adaptive walks with noisy fitness measurements. Mol Divers 1(1):53–68

    Article  Google Scholar 

  • Macia J, Solé RV (2009) Distributed robustness in cellular networks: insights from synthetic evolved circuits. J R Soc Interface 6(33):393–400

    Article  Google Scholar 

  • Masel J, Trotter MV (2010) Robustness and evolvability. Trends Genet 26(9):406–414

    Article  Google Scholar 

  • Milano N, Nolfi S (2016) Robustness to faults promotes evolvability: insights from evolving digital circuits. PLoS ONE 11(7):e0158627

    Article  Google Scholar 

  • Miller J, Hartmann M (2001) Evolving messy gate for fault tolerance: some preliminary findings. In: Proceedings 3rd NASA workshop on evolvable hardware. pp 116–123

  • Miller JF, Thomson P (2000) Cartesian genetic programming. In: Poli R, Banzhaf W, Langdon WB, Miller J, Nordin P, Fogarty TC (eds) Lecture Notes in Computer Science 1802 Genetic programming. Springer, Heidelberg

    Google Scholar 

  • Miller JF, Job D, Vassiley VK (2000) Principles in the evolutionary design of digital circuits. J Genet Progr Evolv Mach 1(1):8–35

    Google Scholar 

  • Miller JF, Thomson P (2000) Cartesian genetic programming. In: Proceedings of the third european conference on genetic programming (EuroGP), vol 1820. Springer, Berlin, pp. 121–132

    Google Scholar 

  • Miller JF, Thompson A, Thompson P, Fogarty T (eds) (2000) Proceedings of the 3rd international conference on evolvable systems: from biology to hardware. Lecture notes on computer science, no. 1801. Springer, Berlin

  • Miller JF (2011) Cartesian genetic programming. Springer, Berlin

    Book  MATH  Google Scholar 

  • Pagliuca P, Milano N, Nolfi S (2018) Maximizing adaptive power in neuroevolution. PLoS ONE 13(7):e0198788

    Article  Google Scholar 

  • Raman K, Wagner A (2011) The evolvability of programmable hardware. J R Soc Interface 8(55):269–281

    Article  Google Scholar 

  • Rana S, Whitlev LD, Cogswell R (1996) Searching in the presence of noise. In: Voigt HM (ed) Parallel problem solving from nature. Lecture Notes in Computer Sciences, 1141. Springer, Berlin, pp 198–207

    Google Scholar 

  • Rechenberg I (1973) Evolutionstrategie—Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuggart

    Google Scholar 

  • Schuster P, Fontana W, Stadler PF, Hofacker IL (1994) From sequences to shapes and back: a case study in RNA secondary structures. Proc R Soc Lond B 255:279–284

    Google Scholar 

  • Sniegowski PD, Murphy HA (2006) Evolvability Curr Biol 16:831–834

    Article  Google Scholar 

  • Sekanina L (2004) Evolvable computing by means of evolvable components. Nat Comput 3(3):253–292

    Article  MathSciNet  MATH  Google Scholar 

  • Thompson A, Layzell P, Zebulum R (1999) Explorations in design space: unconventional electronics design through artificial evolution. IEEE Trans Evol Comput 3(3):167–196

    Article  Google Scholar 

  • Tononi G, Sporns O, Edelman GM (1999) Measures of degeneracy and redundancy in biological networks. Proc Natl Acad Sci USA 96:3257–3262

    Article  Google Scholar 

  • Van Nimwegen E, Crutchfield JP, Huynen M (1999) Neutral evolution of mutational robustness. PNAS 96:9716–9720

    Article  Google Scholar 

  • Wagner A (2008) Robustness and evolvability: a paradox resolved. Proc R Soc B 275:91–100

    Article  Google Scholar 

  • Wagner A (2011) The origins of evolutionary innovations: a theory of transformative change in living systems. Oxford University Press, Oxford

    Book  Google Scholar 

  • Wagner GP, Altenberg L (1996) Perspective: complex adaptations and the evolution of evolvability. Evolution 50:967–976

    Article  Google Scholar 

  • Whitacre JM (2010) Degeneracy: a link between evolvability, robustness and complexity in biological systems. Theor Biol Med Model 7:6

    Article  Google Scholar 

  • Whitley D, Rana S, Heckendorn RB (1998) The island model genetic algorithm: on separability, population size and convergence. J Comput Inf Technol 7:33–47

    Google Scholar 

  • Wilke CO (2001) Adaptive evolution on neutral networks. Bull Math Biol 63:715–730

    Article  MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. Institute of Cognitive Sciences and Technologies, National Research Council (CNR), Via S. Martino della Battaglia, 44, 00185, Rome, Italy

    Nicola Milano, Paolo Pagliuca & Stefano Nolfi

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  1. Nicola Milano
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  2. Paolo Pagliuca
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  3. Stefano Nolfi
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Correspondence to Nicola Milano.

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Milano, N., Pagliuca, P. & Nolfi, S. Robustness, evolvability and phenotypic complexity: insights from evolving digital circuits. Evol. Intel. 12, 83–95 (2019). https://doi.org/10.1007/s12065-018-00197-z

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  • DOI: https://doi.org/10.1007/s12065-018-00197-z

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