Inheritable Epigenetics in Genetic Programming

Classical genetic programming solves problems by applying the Darwinian concepts of selection, survival and reproduction to a population of computer programs. Here we extend the biological analogy to incorporate epigenetic regulation through both learning and evolution. We begin the chapter with a discussion of Darwinian, Lamarckian, and Baldwinian approaches to evolutionary computation and describe how recent findings in biology differ conceptually from the computational strategies that have been proposed. Using inheritable Lamarckian mechanisms as inspiration, we propose a system that allows for updating of individuals in the population during their lifetime while simultaneously preserving both genotypic and phenotypic traits during reproduction. The implementation is made simple through the use of syntax-free, developmental, linear genetic programming. The representation allows for arbitrarily-ordered genomes to be syntactically valid programs, thereby creating a genetic programming approach upon which quasi-uniform epigenetic updating and inheritance can easily be applied. Generational updates are made using an epigenetic hill climber (EHC), and the epigenetic properties of genes are inherited during crossover and mutation. The addition of epigenetics results in faster convergence, less bloat, and an improved ability to find exact solutions on a number of symbolic regression problems.

[1]  Hitoshi Iba,et al.  Inference of differential equation models by genetic programming , 2002, Inf. Sci..

[2]  Ricardo Aler,et al.  Grammatical evolution guided by reinforcement , 2007, 2007 IEEE Congress on Evolutionary Computation.

[3]  B. Dias,et al.  PACAP and the PAC1 Receptor in Post-Traumatic Stress Disorder , 2013, Neuropsychopharmacology.

[4]  Lee Spector,et al.  Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing , 2014, GECCO.

[5]  C. Darwin.  On the Origin of Species by Means of Natural Selection: Or, The Preservation of Favoured Races in the Struggle for Life , 2019 .

[6]  Stephan M. Winkler,et al.  Effects of constant optimization by nonlinear least squares minimization in symbolic regression , 2013, GECCO.

[7]  Miikka Vikkula,et al.  Inherited glomuvenous malformations are caused by the combination of a germline and a somatic "second hit" mutation in the glomulin gene , 2002 .

[8]  Igor Pogribny,et al.  Dose-dependence, sex- and tissue-specificity, and persistence of radiation-induced genomic DNA methylation changes. , 2004, Biochemical and biophysical research communications.

[9]  Hod Lipson,et al.  Automated reverse engineering of nonlinear dynamical systems , 2007, Proceedings of the National Academy of Sciences.

[10]  Hod Lipson,et al.  Age-fitness pareto optimization , 2010, GECCO '10.

[11]  Lee Spector,et al.  Uniform Linear Transformation with Repair and Alternation in Genetic Programming , 2013, GPTP.

[12]  Michael O'Neill,et al.  Genetic Programming and Evolvable Machines Manuscript No. Semantically-based Crossover in Genetic Programming: Application to Real-valued Symbolic Regression , 2022 .

[13]  Leonardo Vanneschi,et al.  Open issues in genetic programming , 2010, Genetic Programming and Evolvable Machines.

[14]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[15]  Charles Darwin,et al.  The Origin of Species by Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life. New York (The Modern Library) 1998. , 1998 .

[16]  L. Darrell Whitley,et al.  Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect , 1993, Evolutionary Computation.

[17]  A. Topchy,et al.  Faster genetic programming based on local gradient search of numeric leaf values , 2001 .

[18]  Lee Spector,et al.  Autoconstructive Evolution: Push, PushGP, and Pushpop , 2001 .

[19]  Christophe G. Giraud-Carrier,et al.  Unifying Learning with Evolution Through Baldwinian Evolution and Lamarckism , 2000, Advances in Computational Intelligence and Learning.

[20]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[21]  Lee Spector,et al.  Genetic Programming and Autoconstructive Evolution with the Push Programming Language , 2002, Genetic Programming and Evolvable Machines.

[22]  Peter A. Jones,et al.  The Role of DNA Methylation in Mammalian Epigenetics , 2001, Science.

[23]  L. Darrell Whitley,et al.  Lamarckian Evolution, The Baldwin Effect and Function Optimization , 1994, PPSN.

[24]  S. Edvinsson,et al.  Cardiovascular and diabetes mortality determined by nutrition during parents' and grandparents' slow growth period , 2002, European Journal of Human Genetics.

[25]  R. Pfeifer,et al.  Repeated structure and dissociation of genotypic and phenotypic complexity in artificial ontogeny , 2001 .

[26]  Julian Francis Miller,et al.  Cartesian genetic programming , 2000, GECCO '10.

[27]  Hod Lipson,et al.  Comparison of tree and graph encodings as function of problem complexity , 2007, GECCO '07.

[28]  Krzysztof Krawiec,et al.  Locally geometric semantic crossover: a study on the roles of semantics and homology in recombination operators , 2012, Genetic Programming and Evolvable Machines.

[29]  I. Tanev,et al.  Epigenetic programming: Genetic programming incorporating epigenetic learning through modification of histones , 2008, Inf. Sci..

[30]  Riccardo Poli,et al.  Smooth Uniform Crossover with Smooth Point Mutation in Genetic Programming: A Preliminary Study , 1999, EuroGP.

[31]  B M Turner,et al.  Histone acetylation and an epigenetic code. , 2000, BioEssays : news and reviews in molecular, cellular and developmental biology.

[32]  Alessandro Fontana,et al.  Epigenetic Tracking: Biological Implications , 2009, ECAL.

[33]  Robin Holliday,et al.  Epigenetics: A Historical Overview , 2006, Epigenetics.

[34]  Wojciech Jaskowski,et al.  Better GP benchmarks: community survey results and proposals , 2012, Genetic Programming and Evolvable Machines.

[35]  Wolfgang Banzhaf,et al.  Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming , 1994, PPSN.

[36]  E. Jablonka,et al.  The Changing Concept of Epigenetics , 2002, Annals of the New York Academy of Sciences.

[37]  P. Nordin,et al.  Explicitly defined introns and destructive crossover in genetic programming , 1996 .

[38]  Brian J. Ross,et al.  A Lamarckian Evolution Strategy for Genetic Algorithms , 1998, Practical Handbook of Genetic Algorithms.

[39]  Paulien Hogeweg,et al.  Evolutionary Consequences of Coevolving Targets , 1997, Evolutionary Computation.

[40]  Samir W. Mahfoud.  Niching methods for genetic algorithms , 1996 .

[41]  Nguyen Xuan Hoai,et al.  Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: the comparative results , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[42]  B. Dias,et al.  Parental olfactory experience influences behavior and neural structure in subsequent generations , 2013, Nature Neuroscience.