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Automatic python programming using stack-based genetic programming

Hyun soo Park, Kyung Joong Kim
2012 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12  
In this research, we propose to evolve bytecode of Python programming language by stack-based genetic programming.  ...  Traditional genetic programming uses tree-like data structure to represent a program.  ...  CONCLUSION AND FUTURE WORK We use the stack-based Genetic Programming to evolve Python bytecode. To the best of our knowledge, it is the first time to evolve the Python in the bytecode level.  ... 
doi:10.1145/2330784.2330899 dblp:conf/gecco/ParkK12 fatcat:cpx66rdwrfhshaj2lndwylwfau

Code building genetic programming

Edward Pantridge, Lee Spector
2020 Proceedings of the 2020 Genetic and Evolutionary Computation Conference  
In recent years the field of genetic programming has made significant advances towards automatic programming.  ...  Few, if any, genetic programming methods for program synthesis have convincingly demonstrated the capability of synthesizing programs that use arbitrary data types, data structures, and specifications  ...  This material is based upon work supported by the National Science Foundation under Grant No. 1617087.  ... 
doi:10.1145/3377930.3390239 dblp:conf/gecco/PantridgeS20 fatcat:ev2o5445qbftrfo67yd73ygy7e

Recent Developments in Program Synthesis with Evolutionary Algorithms [article]

Dominik Sobania, Dirk Schweim, Franz Rothlauf
2021 arXiv   pre-print
The most influential approaches we identify are stack-based, grammar-guided, as well as linear genetic programming.  ...  The automatic generation of computer programs is one of the main applications with practical relevance in the field of evolutionary computation.  ...  In evolutionary computation, especially genetic programming (GP) is known for the automatic generation of computer programs.  ... 
arXiv:2108.12227v1 fatcat:rtzu4cilpnaq5kjs2kf4syuqom

Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming [article]

Dominik Sobania, Martin Briesch, Franz Rothlauf
2021 arXiv   pre-print
We find that the performance of the two approaches on the benchmark problems is quite similar, however, in comparison to GitHub Copilot, the program synthesis approaches based on genetic programming are  ...  This model has been extensively studied in the field of deep learning, however, a comparison to genetic programming, which is also known for its performance in automatic program synthesis, has not yet  ...  The stack-based approach mainly used in the literature is PushGP [34, 35] , which is based on the stack-based programming language Push.  ... 
arXiv:2111.07875v1 fatcat:stxzpmcmo5fsnckidpnmxub2cm

STXMPy: a new software package for automated region of interest selection and statistical analysis of XANES data

Tamás Haraszti, Michael Grunze, Michael G Anderson
2010 Chemistry Central Journal  
It is open source, cross platform, and offers rapid script development using the interpreted Python language.  ...  Soft X-ray spectromicroscopy based absorption near-edge structure analysis, is a spectroscopic technique useful for investigating sample composition at a nanoscale of resolution.  ...  Separate programs were written in the Python programming language to process and visualize data sets both in interactive and batch processing.  ... 
doi:10.1186/1752-153x-4-11 pmid:20525317 pmcid:PMC2891742 fatcat:4lcna562v5cg7mgvlvos6jkvyi

What's in an Evolved Name? The Evolution of Modularity via Tag-Based Reference [chapter]

Lee Spector, Kyle Harrington, Brian Martin, Thomas Helmuth
2011 Genetic and Evolutionary Computation  
We demonstrate the use of tag-based names, we describe some of the ways in which they may help to extend the power and reach of genetic programming systems, and we look at the ways that tag-based names  ...  In this chapter we describe a new approach to names in genetic programming that is based on Holland's concept of tags.  ...  Neimark contributed to conversations in which some of the ideas used in this work were refined.  ... 
doi:10.1007/978-1-4614-1770-5_1 fatcat:b4fbe36sebfhtdhikeb6vlro7m

Choose your programming copilot

Dominik Sobania, Martin Briesch, Franz Rothlauf
2022 Proceedings of the Genetic and Evolutionary Computation Conference  
CCS CONCEPTS • Software and its engineering → Search-based software engineering; Genetic programming; • Computing methodologies → Neural networks.  ...  This model has been extensively studied in the field of deep learning, however, a comparison to genetic programming, which is also known for its performance in automatic program synthesis, has not yet  ...  The stack-based approach mainly used in the literature is PushGP [34, 35] , which is based on the stack-based programming language Push.  ... 
doi:10.1145/3512290.3528700 fatcat:zsxwmjlgxfegdkyr6x2dpvt6um

Warfarin dose estimation on multiple datasets with automated hyperparameter optimisation and a novel software framework [article]

Gianluca Truda, Patrick Marais
2020 arXiv   pre-print
We also introduced genetic programming to automatically optimise model architectures and hyperparameters without human guidance.  ...  Support vectors and linear regression were amongst the top performers in both datasets and performed comparably to recent stacked ensemble approaches, whilst neural networks were one of the worst performers  ...  This study made use of the open-sourced Tree-based Pipeline Optimisation Tool (TPOT) to generate high-performing models through genetic programming [23] .  ... 
arXiv:1907.05363v4 fatcat:mhbodip3trdydlm5mbpfffoahu

A Survey of Genetic Programming and Its Applications

2019 KSII Transactions on Internet and Information Systems  
But one of the most significant uses of GAs is the automatic generation of programs.  ...  Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA).  ...  Stack-based Genetic Programming (SGP) In this type of GPs, the programs execute on a stack-based virtual machine.  ... 
doi:10.3837/tiis.2019.04.002 fatcat:wsoz6h5dbjhdphyytdqsbr42di

Auto Modelling for Machine Learning: A Comparison Implementation between RapidMiner and Python

Norhayati Baharun, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, MALAYSIA, Nor Faezah Mohamad Razi, Suraya Masrom, Nor Ain Mohamad Yusri, Abdullah Sani Abd Rahman
2022 International Journal of Emerging Technology and Advanced Engineering  
To compare the results of modelling from the different approaches, the Airbnb hospitality dataset has been used as a case study for predicting the hospitality prices.  ...  While a bigger impact has been reported on business intelligence models, there has been very little effort that investigates the deployment of business intelligence models based on auto modelling approaches  ...  Tree-based Pipeline Optimization Tool (TPOT) [12] is another recent method that used Genetic Programming optimization as the underlying algorithm for the hyper-parameters tuning of machine learning.  ... 
doi:10.46338/ijetae0522_03 fatcat:qnzza2ctr5dj7dr354gkl5cap4

NeuroFramework: A package based on neuroevolutionary algorithms to estimate the melting temperature of ionic liquids

Jorge Alberto Cerecedo-Cordoba, Juan Frausto-Solís, Juan Javier González Barbosa
2020 SoftwareX  
This software includes a wrapper for Python for machine learning tasks, especially regression; however, the user can adapt this package for classification.  ...  This algorithm offers flexibility in its design by the stacking of operators to form an algorithm prototype.  ...  In the example, we showed how to use NeuroFramework in the Python programming language. The user can use our software as a tool in the user machine learning tasks.  ... 
doi:10.1016/j.softx.2020.100448 fatcat:iyhax46jmrakvomiwgojpelibe

Why functional program synthesis matters (in the realm of genetic programming)

Fraser Garrow, Michael A. Lones, Robert Stewart
2022 Proceedings of the Genetic and Evolutionary Computation Conference Companion  
CCS CONCEPTS • Software and its engineering → Genetic programming; Functional languages; Automatic programming.  ...  In Genetic Programming (GP) systems, particularly those that target general program synthesis problems, it is common to use imperative programming languages to represent evolving code.  ...  Program synthesis is one aspect of automatic programming where the goal is to automatically generate programs based on some specification or user prompt.  ... 
doi:10.1145/3520304.3534045 fatcat:oosgi6tdzncszjayjzbtbl73c4

Predicting Good Configurations for GitHub and Stack Overflow Topic Models [article]

Christoph Treude, Markus Wagner
2019 arXiv   pre-print
We find that (1) popular rules of thumb for topic modelling parameter configuration are not applicable to the corpora used in our experiments, (2) corpora sampled from GitHub and Stack Overflow have different  ...  eight programming languages, and (iii) an analysis of corpus feature importance via per-corpus LDA configuration.  ...  We acknowledge the support by the HPI Future SOC Lab, who granted us access to their computing resources.  ... 
arXiv:1804.04749v3 fatcat:xh5y3x2wvffh7hpupatc6tgesa

A New Domain Specific Scripting Language for Automated Machine Learning Pipeline

2019 International journal of recent technology and engineering  
Research has proved that Genetic Programming is highly useful to find the best pipeline of an automated machine learning model.  ...  However, in respond to the implementation difficulty, there exists a limited software tool that support easy implementation for automated machine learning based on Genetic Programming.  ...  Utilizing stacking and metadata to automate the model selection and hyper tuning, Automatic Ensemble [22] is the most recent work found in the literature.  ... 
doi:10.35940/ijrte.b1082.0982s1119 fatcat:vqphnilmdfb2remz37mscf4osy

Program Trace Optimization [chapter]

Alberto Moraglio, James McDermott
2018 Lecture Notes in Computer Science  
New problem definitions and new generic search algorithms can be added to PTO easily and independently, and any algorithm can be used on any problem.  ...  We introduce Program Trace Optimization (PTO), a system for 'universal heuristic optimization made easy'. This is achieved by strictly separating the problem from the search algorithm.  ...  PTO then automatically carries out design based on user's intuition on the problem.  ... 
doi:10.1007/978-3-319-99259-4_27 fatcat:6byfh5rhfjbpzjggtue7pu4aci
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