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Environments with local scopes for modules in genetic programming

Anil Kumar Saini, Lee Spector, Thomas Helmuth
2022 Proceedings of the Genetic and Evolutionary Computation Conference Companion  
The PushGP genetic programming system, which has produced state-of-the-art results in application areas, including software synthesis, allows programs to label sequences of instructions as modules.  ...  To rectify this, and to ensure that modules return single values, we implement scoped environments in PushGP, using a method that allows for the dynamic definition of arbitrary modules in a multi-type  ...  We also include one symbolic regression problem.  ... 
doi:10.1145/3520304.3528958 fatcat:tnw7ixpf6ravhkmoc5gpifts54

A multi-chromosome approach to standard and embedded cartesian genetic programming

James Alfred Walker, Julian Francis Miller, Rachel Cavill
2006 Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06  
Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) that can automatically acquire, evolve and re-use partial solutions in the form of modules.  ...  In this paper, we introduce for the first time a new multi-chromosome approach to CGP and ECGP that allows difficult problems with multiple outputs to be broken down into many smaller, simpler problems  ...  BACKGROUND Cartesian Genetic Programming (CGP) CGP was invented by Miller and Thomson [13] for the purpose of evolving digital circuits and represents a program as a directed graph (that for feed-forward  ... 
doi:10.1145/1143997.1144153 dblp:conf/gecco/WalkerMC06 fatcat:j5mtamktuvd5flp5v35rxkk5wi

Cartesian genetic programming: its status and future

Julian Francis Miller
2019 Genetic Programming and Evolvable Machines  
Cartesian genetic programming, a well-established method of genetic programming, is approximately 20 years old. It represents solutions to computational problems as graphs.  ...  In the process, we make many suggestions for further work which could improve the efficiency of the CGP for solving computational problems.  ...  Acknowledgements Thanks to the anonymous reviewers and to Dennis Wilson for their helpful comments.  ... 
doi:10.1007/s10710-019-09360-6 fatcat:vocd7cqshndefkib6nvbhu7gxa

Sequential Symbolic Regression with Genetic Programming [chapter]

Luiz Otávio V.B. Oliveira, Fernando E.B. Otero, Gisele L. Pappa, Julio Albinati
2015 Genetic and Evolutionary Computation  
This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression.  ...  The method was tested in eight polynomial functions, and compared with canonical genetic programming (GP) and geometric semantic genetic programming (SGP).  ...  (2008) in the context of Embedded Cartesian Genetic Programming (ECGP), with the extension of the use of module-altering operators (module point mutation, add-input, add-output, remove-input and remove-output  ... 
doi:10.1007/978-3-319-16030-6_5 dblp:conf/gptp/OliveiraOPA14 fatcat:fko7mhdpejbsll3ana5yixfzbu

The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming

J.A. Walker, J.F. Miller
2008 IEEE Transactions on Evolutionary Computation  
This paper presents a generalization of the graphbased genetic programming (GP) technique known as Cartesian genetic programming (CGP).  ...  To benchmark the new technique, we have tested it on: various digital circuit problems, two symbolic regression problems, the lawnmower problem, and the hierarchical if-and-only-if problem.  ...  Yao, and the anonymous reviewers for all of their help and useful comments.  ... 
doi:10.1109/tevc.2007.903549 fatcat:vdjbnadxy5e4jkww66ptlizlcu

Towards compositional coevolution in evolutionary circuit design

Michaela Sikulova, Gergely Komjathy, Lukas Sekanina
2014 2014 IEEE International Conference on Evolvable Systems  
The proposed method is embedded into Cartesian genetic programming (CGP).  ...  The filters evolved using the proposed coevolutionary method show a higher quality of filtering in comparison with filters utilizing independently evolved modules.  ...  Cartesian Genetic Programming Cartesian genetic programming (CGP) is a variant of genetic programming (GP) that uses a specific encoding of directed acyclic graphs and a mutation-based search.  ... 
doi:10.1109/ices.2014.7008735 dblp:conf/ices/SikulovaKS14 fatcat:jfmrsalvirbqbldpoccaks4rri

A Review of Genetic Programming Popular Techniques, Fundamental Aspects, Software Tools and Applications

Davut ARI, Barış Baykant ALAGÖZ
2021 Sakarya University Journal of Science  
Looking at the literature, it is seen that GP has been widely preferred in the problems that need symbolic regression for data modeling [6] .  ...  Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers.  ...  Pappa, 'A multi-objective approach for symbolic regression with semantic genetic programming', Proc. -2019 Brazilian Conf. Intell. Syst. BRACIS 2019, ,pp. 66-71, 2019.  ... 
doi:10.16984/saufenbilder.793333 fatcat:yyopflbk4vgwvbcitrjnwtf4xe

Analyzing Module Usage in Grammatical Evolution [chapter]

John Mark Swafford, Erik Hemberg, Michael O'Neill, Anthony Brabazon
2012 Lecture Notes in Computer Science  
Being able to exploit modularity in genetic programming (GP) is an open issue and a promising vein of research.  ...  The results suggest directions for future work in improving module manipulation via crossover and mutation and module usage in the population.  ...  [13] , cites modularity as an important open topic in genetic programming (GP) [7] .  ... 
doi:10.1007/978-3-642-32937-1_35 fatcat:ysnsqfswkfc4jp7mutna6tkaka

Differentiable Genetic Programming [article]

Dario Izzo and Francesco Biscani and Alessio Mereta
2016 arXiv   pre-print
The resulting machine learning framework is called differentiable Cartesian Genetic Programming (dCGP).  ...  Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning.  ...  Program encoding To represent our functional programs we use the Cartesian Genetic Programming framework [16] .  ... 
arXiv:1611.04766v1 fatcat:uav2fmxgbrer5ppwkgrbzseiee

Analytic Solutions to Differential Equations under Graph-Based Genetic Programming [chapter]

Tom Seaton, Gavin Brown, Julian F. Miller
2010 Lecture Notes in Computer Science  
Cartesian Genetic Programming (CGP) is applied to solving differential equations (DE). We illustrate that repeated elements in analytic solutions to DE can be exploited under GP.  ...  To explore this concept, we carry out an analysis using a variant on an established graph-based approach, Cartesian Genetic Programming (CGP) [8] , across a set of benchmark DE.  ...  Koza briefly addressed learning solutions to ODEs in his seminal work on problems for Tree Genetic Programming (GP) [4] .  ... 
doi:10.1007/978-3-642-12148-7_20 fatcat:jcv5xgt4vjecbaezkuitf6crim

Tag-based modularity in tree-based genetic programming

Lee Spector, Kyle Harrington, Thomas Helmuth
2012 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO '12  
Several techniques have been developed for allowing genetic programming systems to produce programs that make use of subroutines, macros, and other modular program structures.  ...  Following a suggestion in the GECCO-2011 publication on this technique we show here how tag-based modules can be incorporated into a more standard tree-based genetic programming system.  ...  comments on a draft, to Josiah Erikson for systems support, and to Hampshire College for support for the Hampshire College Institute for Computational Intelligence.  ... 
doi:10.1145/2330163.2330276 dblp:conf/gecco/SpectorHH12 fatcat:g5t7ixdklvhrxf72wxwtx4ek3m

Population variation in genetic programming

Peyman Kouchakpour, Anthony Zaknich, Thomas Bräunl
2007 Information Sciences  
with the aim of reducing the computational effort with respect to that of Standard Genetic Programming.  ...  Finally, further interesting research potentials for population variation are identified together with some of the open areas of research within the Genetic Programming and also possible future trends  ...  The Cartesian genetic programming was extended by utilizing automatic module acquisition in [312] .  ... 
doi:10.1016/j.ins.2007.02.032 fatcat:ts4xcwqlx5hgpfoqsp3u5ec24y

sEMG-based Endpoint Stiffness Estimation of Human Arm using Gene Expression Programming

Jiang Zainan, Yang Fan, Li Chongyang, Liu Daxiang, Wang Chenliang, Li Tianhui, Liu Hong
2019 Journal of Physics, Conference Series  
And experimental results show that nonlinear models such as GEP models in this paper have higher correlation and lower RMSE (root mean square error) than regression stiffness using linear regression models  ...  And the endpoint stiffness of the human arm is highly correlated with the surface electromyography (sEMG) produced by the contraction of the muscles.  ...  GEP is a function mining algorithm proposed by Ferreira, which combines the advantages of Genetic Algorithm (GA) and Genetic Programming (GP) [9] .  ... 
doi:10.1088/1742-6596/1267/1/012016 fatcat:gaitgvilwrd2lew6ac64fojai4

Evolving Parsimonious Networks by Mixing Activation Functions [article]

Alexander Hagg, Maximilian Mensing, Alexander Asteroth
2017 arXiv   pre-print
For a number of regression and classification benchmarks it is shown that, (1) qualitatively different activation functions lead to different results in homogeneous networks, (2) the heterogeneous version  ...  and in some cases the topology, but little work has been done to analyze the effect of evolving the activation functions of individual nodes on network size, which is important when training networks with  ...  Khan et al. introduce an extension to Cartesian Genetic Programming (CGP) [14, 17] , CGPANN, in which they de ne neurons as nodes in the cartesian genotype.  ... 
arXiv:1703.07122v1 fatcat:ftgbguraprgx7kanwn5sp3nk3u

The Design of Evolvable Hardware Image Filters Using Fuzzy Sets

Chih-Hung Wu, Chien-Jung Chen, Chin Yuan Chiang
2012 2012 Conference on Technologies and Applications of Artificial Intelligence  
Three filtering modules for pixels with various degrees of noise contamination are trained supervisedly by Cartesian genetic programming.  ...  The recovery of a noisy pixel is the fuzzy weighted sum of the output from the three filtering modules.  ...  The EA employed in this study is Cartesian Genetic Programming (CGP) [20] . CGP is a supervised genetic algorithm for symbolic regression.  ... 
doi:10.1109/taai.2012.14 dblp:conf/taai/WuCC12 fatcat:3lmjziic3fh3ro6s6um536hyka
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