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Abdelmalek:2009:JAMDS Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming
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Abdulhamid:2011:ICARA Genetic programming for evolving programs with loop structures for classification tasks
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Abdulkarimova:2025:GPEM Harnessing evolutionary algorithms for enhanced characterization of ENSO events
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Abdullah:thesis Android Malware Detection System using Genetic Programming
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Abdul-Rahim:2006:ccis An Empirical Comparison of Code Size Limit in Auto-Constructive Artificial Life
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Abdulrahman:2020:IJCA Classification of Retina Diseases from OCT using Genetic Programming
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Abednego:2011:ICEEI Genetic programming hyper-heuristic for solving dynamic production scheduling problem
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Abhishek:2014:PMS Comparing Predictability of Genetic Programming and ANFIS on Drilling Performance Modeling for GFRP Composites
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Abid:2012:GPnew Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models
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ABOELELA:2022:RE Estimating the subgrade reaction at deep braced excavation bed in dry granular soil using genetic programming (GP)
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Abooali:2014:JNGSE Estimation of dynamic viscosity of natural gas based on genetic programming methodology
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ABOOALI:2019:JPSE A new empirical model for estimation of crude oil/brine interfacial tension using genetic programming approach
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ABOOALI:2020:Fuel Characterization of physico-chemical properties of biodiesel components using smart data mining approaches
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DBLP:journals/nca/AbooaliK19 New predictive method for estimation of natural gas hydrate formation temperature using genetic programming
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Affenzeller:2023:GPTP GP in Prescriptive Analytics
MichaelAffenzeller.html
Afshar:2017:RSE The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products
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Timperley:2018:GI A Turing Test for Genetic Improvement
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RobertFeldt.html
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Afzal08e Suitability of Genetic Programming for Software Reliability Growth Modeling
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RichardTorkar.html
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Afzal08d A comparative evaluation of using genetic programming for predicting fault count data
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RichardTorkar.html
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Afzal08b Prediction of fault count data using genetic programming
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RichardTorkar.html
RobertFeldt.html
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RichardTorkar.html
RobertFeldt.html
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Afzal2009 A systematic review of search-based testing for non-functional system properties
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RichardTorkar.html
RobertFeldt.html
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Afzal:Licentiate Search-Based Approaches to Software Fault Prediction and Software Testing
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Afzal:2010:ECoaSE Genetic Programming for Cross-Release Fault Count Predictions in Large and Complex Software Projects
WasifAfzal.html
RichardTorkar.html
RobertFeldt.html
TonyGorschek.html
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RichardTorkar.html
RobertFeldt.html
GregerWikstrand.html
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Afzal:2010:APSEC Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness
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RichardTorkar.html
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Afzal:thesis Search-Based Prediction of Software Quality: Evaluations And Comparisons
WasifAfzal.html
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afzali:2018:AJCAI A Genetic Programming Approach for Constructing Foreground and Background Saliency Features for Salient Object Detection
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HarithAl-Sahaf.html
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Afzali:2019:evoapplications Genetic Programming for Feature Selection and Feature Combination in Salient Object Detection
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HarithAl-Sahaf.html
BingXue.html
ChristopherHollitt.html
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eurogp06:AgapitosLucas Learning Recursive Functions with Object Oriented Genetic Programming
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SimonMLucas.html
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SimonMLucas.html
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SimonMLucas.html
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eurogp07:agapitos2 Evolving Modular Recursive Sorting Algorithms
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SimonMLucas.html
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JulianTogelius.html
SimonMLucas.html
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JulianTogelius.html
SimonMLucas.html
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Agapitos:2008:gecco Learning to recognise mental activities: genetic programming of stateful classifiers for brain-computer interfacing
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MatthewDyson.html
SimonMLucas.html
FranciscoSepulveda.html
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Agapitos2:2008:gecco On the genetic programming of time-series predictors for supply chain management
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YevgeniyaKovalchuk.html
SimonMLucas.html
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Agapitos:2008:CIG Generating Diverse Opponents with Multiobjective Evolution
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SimonMLucas.html
JurgenSchmidhuber.html
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agapitos_etal:ppsn2010 Evolutionary Learning of Technical Trading Rules without Data-mining Bias
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MichaelO'Neill.html
AnthonyBrabazon.html
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Agapitos:2010:AIAI Evolutionary Prediction of Total Electron Content over Cyprus
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AndreasConstantinides.html
HarisHaralambous.html
HarrisPapadopoulos.html
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agapitosetal:2010:cfe Promoting the generalisation of genetically induced trading rules
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MichaelO'Neill.html
AnthonyBrabazon.html
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agapitos:2011:EuroGP Maximum Margin Decision Surfaces for Increased Generalisation in Evolutionary Decision Tree Learning
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MichaelO'Neill.html
AnthonyBrabazon.html
TheodorosTheodoridis.html
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Agapitos:2011:GECCOcomp Stateful program representations for evolving technical trading rules
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MichaelO'Neill.html
AnthonyBrabazon.html
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Agapitos:2011:CIG Learning Environment Models in Car Racing Using Stateful Genetic Programming
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MichaelO'Neill.html
AnthonyBrabazon.html
TheodorosTheodoridis.html
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Agapitos:NCFE:2011 An Evolutionary Algorithmic Investigation of US Corporate Payout Policy
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CalMuckley.html
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agapitos:evoapps12 Evolving Seasonal Forecasting Models with Genetic Programming in the Context of Pricing Weather-Derivatives
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MichaelO'Neill.html
AnthonyBrabazon.html
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Agapitos:FDMCI:2012 Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather Derivatives
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MichaelO'Neill.html
AnthonyBrabazon.html
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conf/ppsn/Agapitos12 Controlling Overfitting in Symbolic Regression Based on a Bias/Variance Error Decomposition
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AnthonyBrabazon.html
MichaelO'Neill.html
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agapitos:2013:EuroGP Adaptive Distance Metrics for Nearest Neighbour Classification based on Genetic Programming
AlexandrosAgapitos.html
MichaelO'Neill.html
AnthonyBrabazon.html
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agapitos:2014:EuroGP Higher Order Functions for Kernel Regression
AlexandrosAgapitos.html
JamesMcDermott.html
MichaelO'Neill.html
AhmedKattan.html
AnthonyBrabazon.html
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Agapitos:2014:CEC Ensemble Bayesian Model Averaging in Genetic Programming
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MichaelO'Neill.html
AnthonyBrabazon.html
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agapitos:cec2015 Deep Evolution of Feature Representations for Handwritten Digit Recognition
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MichaelO'Neill.html
MiguelNicolau.html
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EvoBafin16Agapitosetal Genetic Programming with Memory For Financial Trading
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Agapitos:2016:GPEM Recursion in tree-based genetic programming
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AlexandrosAgapitos.html
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agapow:1996:cbecv Computational Brittleness and the Evolution of Computer Viruses
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Al-Madi:2013:SSCI Improving genetic programming classification for binary and multiclass datasets
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AL-Madi:2013:GECCOcomp Segment-based genetic programming
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Al-Madi:2013:nabic Scaling Genetic Programming for Data Classification using MapReduce Methodology
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Al-Madi:thesis Improved genetic programming techniques for data classification
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Al-Maqaleh:2007:isi Genetic Programming Approach to Hierarchical Production Rule Discovery
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Al-Rahamneh:2011:JSEA A New Software Reliability Growth Model: Genetic-Programming-Based Approach
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journals/corr/abs-2001-09923 Applying Gene Expression Programming for Solving One-Dimensional Bin-Packing Problems
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AHGandomi.html
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XiangdongChe.html
http___dx.doi.org_10.1007_978-0-387-87623-8_16 http://dx.doi.org/10.1007/978-0-387-87623-8_16
DBLP:journals/evi/AliM20 Difficult first strategy GP: an inexpensive sampling technique to improve the performance of genetic programming
MuhammadQuamberAli.html
HammadMajeed.html
https___doi.org_10.1007_s12065-020-00355-2 https://doi.org/10.1007/s12065-020-00355-2
http___dx.doi.org_10.1007_s12065-020-00355-2 http://dx.doi.org/10.1007/s12065-020-00355-2
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DBLP:journals/nca/AliJAAM22 Multi-objective Lyapunov-based controller design for nonlinear systems via genetic programming
MirMasoudAleAli.html
AliJamali.html
AmirhosseinAsgharnia.html
RezaAnsari.html
RammohanMallipeddi.html
https___rdcu.be_dl3Cd https://rdcu.be/dl3Cd
https___doi.org_10.1007_s00521-021-06453-1 https://doi.org/10.1007/s00521-021-06453-1
http___dx.doi.org_10.1007_s00521-021-06453-1 http://dx.doi.org/10.1007/s00521-021-06453-1
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ALI:2018:AFM Cotton yield prediction with Markov Chain Monte Carlo-based simulation model integrated with genetic programing algorithm: A new hybrid copula-driven approach
MumtazAli.html
RavineshCDeo.html
NathanJDowns.html
TekMaraseni.html
http___dx.doi.org_10.1016_j.agrformet.2018.09.002 http://dx.doi.org/10.1016/j.agrformet.2018.09.002
http___www.sciencedirect.com_science_article_pii_S0168192318302971 http://www.sciencedirect.com/science/article/pii/S0168192318302971
ALI:2020:HPM Chapter 2 - Modeling wheat yield with data-intelligent algorithms: artificial neural network versus genetic programming and minimax probability machine regression
MumtazAli.html
RavineshCDeo.html
http___dx.doi.org_10.1016_B978-0-12-816514-0.00002-3 http://dx.doi.org/10.1016/B978-0-12-816514-0.00002-3
http___www.sciencedirect.com_science_article_pii_B9780128165140000023 http://www.sciencedirect.com/science/article/pii/B9780128165140000023
Ali:2015:JBI Can-Evo-Ens: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences
SafdarAli.html
AbdulMajid.html
http___www.sciencedirect.com_science_article_pii_S1532046415000064 http://www.sciencedirect.com/science/article/pii/S1532046415000064
http___dx.doi.org_10.1016_j.jbi.2015.01.004 http://dx.doi.org/10.1016/j.jbi.2015.01.004
Ali:thesis Intelligent Decision Making Ensemble Classification System for Breast Cancer Prediction
SafdarAli.html
http___faculty.pieas.edu.pk_abdulmajid_ http://faculty.pieas.edu.pk/abdulmajid/
http___prr.hec.gov.pk_jspui_handle_123456789__7613 http://prr.hec.gov.pk/jspui/handle/123456789//7613
https___prr.hec.gov.pk_jspui_bitstream_123456789_7613_1_Safdar-Ali_Computer_and_Information_Sciences_2015_PIEAS_ISD_20PDF.pdf https://prr.hec.gov.pk/jspui/bitstream/123456789/7613/1/Safdar-Ali_Computer_and_Information_Sciences_2015_PIEAS_ISD%20PDF.pdf
Ali:2010:ieeeTSE A Systematic Review of the Application and Empirical Investigation of Search-Based Test-Case Generation
ShaukatAli.html
LionelCBriand.html
HadiHemmati.html
RajwinderKaurPanesar-Walawege.html
http___ieeexplore.ieee.org_stamp_stamp.jsp_tp__arnumber_5210118_isnumber_4359463 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5210118&isnumber=4359463
http___dx.doi.org_10.1109_TSE.2009.52 http://dx.doi.org/10.1109/TSE.2009.52
Ali:2012:SETIT Miner for OACCR: Case of medical data analysis in knowledge discovery
SamaherHusseinAli.html
http___dx.doi.org_10.1109_SETIT.2012.6482043 http://dx.doi.org/10.1109/SETIT.2012.6482043
conf/icaart/Ali0NR21 AutoGE: A Tool for Estimation of Grammatical Evolution Models
MuhammadSarmadAli.html
MeghanaKshirsagar.html
EnriqueNaredo.html
ConorRyan.html
http___dx.doi.org_10.5220_0010393012741281 http://dx.doi.org/10.5220/0010393012741281
conf/ijcci/Ali0NR21 Towards Automatic Grammatical Evolution for Real-world Symbolic Regression
MuhammadSarmadAli.html
MeghanaKshirsagar.html
EnriqueNaredo.html
ConorRyan.html
http___dx.doi.org_10.5220_0010691500003063 http://dx.doi.org/10.5220/0010691500003063
ali:2022:GECCO Automated Grammar-based Feature Selection in Symbolic Regression
MuhammadSarmadAli.html
MeghanaKshirsagar.html
EnriqueNaredo.html
ConorRyan.html
http___dx.doi.org_10.1145_3512290.3528852 http://dx.doi.org/10.1145/3512290.3528852
Ali:2022:ICAIoT Optimization of the Number and Placement of Routers in Wireless Mesh Networks
MohammedSadeqAliAli.html
MesutCevik.html
http___dx.doi.org_10.1109_ICAIoT57170.2022.10121861 http://dx.doi.org/10.1109/ICAIoT57170.2022.10121861
ALI:2023:istruc Artificial intelligent techniques for prediction of rock strength and deformation properties - A review
MujahidAli.html
SaiHinLai.html
http___dx.doi.org_10.1016_j.istruc.2023.06.131 http://dx.doi.org/10.1016/j.istruc.2023.06.131
https___www.sciencedirect.com_science_article_pii_S2352012423008901 https://www.sciencedirect.com/science/article/pii/S2352012423008901
Alibekov:2016:CDC Symbolic method for deriving policy in reinforcement learning
EduardAlibekov.html
JiriKubalik.html
RobertBabuska.html
http___dx.doi.org_10.1109_CDC.2016.7798684 http://dx.doi.org/10.1109/CDC.2016.7798684
Alibekov:thesis Symbolic Regression for Reinforcement Learning in Continuous Spaces
EduardAlibekov.html
https___cyber.felk.cvut.cz_news_eduard-alibekov-defended-his-ph-d-thesis_ https://cyber.felk.cvut.cz/news/eduard-alibekov-defended-his-ph-d-thesis/
http___hdl.handle.net_10467_98283 http://hdl.handle.net/10467/98283
https___dspace.cvut.cz_handle_10467_98283 https://dspace.cvut.cz/handle/10467/98283
https___dspace.cvut.cz_bitstream_handle_10467_98283_F3-D-2021-Alibekov-Eduard-phd_ready.pdf https://dspace.cvut.cz/bitstream/handle/10467/98283/F3-D-2021-Alibekov-Eduard-phd_ready.pdf
ALIDOUST:2021:JCP Prediction of the shear modulus of municipal solid waste (MSW): An application of machine learning techniques
PouryaAlidoust.html
MohsenKeramati.html
PouriaHamidian.html
AmirTavanaAmlashi.html
MahsaModiriGharehveran.html
AliBehnood.html
http___dx.doi.org_10.1016_j.jclepro.2021.127053 http://dx.doi.org/10.1016/j.jclepro.2021.127053
https___www.sciencedirect.com_science_article_pii_S0959652621012725 https://www.sciencedirect.com/science/article/pii/S0959652621012725
Aliehyaei:2014:SKIMA Ant Colony Optimization, Genetic Programming and a hybrid approach for credit scoring: A comparative study
RAliehyaei.html
SKhan.html
http___dx.doi.org_10.1109_SKIMA.2014.7083391 http://dx.doi.org/10.1109/SKIMA.2014.7083391
AliGhorbani2010620 Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks
MohammadAliGhorbani.html
RahmanKhatibi.html
AliAytek.html
OlegMakarynskyy.html
JalalShiri.html
http___dx.doi.org_10.1016_j.cageo.2009.09.014 http://dx.doi.org/10.1016/j.cageo.2009.09.014
http___www.sciencedirect.com_science_article_B6V7D-4YCS020-1_2_514d629e145e62f37dbf599a1a7608a9 http://www.sciencedirect.com/science/article/B6V7D-4YCS020-1/2/514d629e145e62f37dbf599a1a7608a9
AliGhorbani:2012:GPnew Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models
MohammadAliGhorbani.html
RahmanKhatibi.html
HAsadi.html
PYousefi.html
http___dx.doi.org_10.5772_47801 http://dx.doi.org/10.5772/47801
ALISHAH:2024:fuel Predictive models of laminar flame speed in NH3/H2/O3/air mixtures using multi-gene genetic programming under varied fuelling conditions
ZubairAliShah.html
GMarseglia.html
MariaGraziaDeGiorgi.html
http___dx.doi.org_10.1016_j.fuel.2024.131652 http://dx.doi.org/10.1016/j.fuel.2024.131652
https___www.sciencedirect.com_science_article_pii_S0016236124008007 https://www.sciencedirect.com/science/article/pii/S0016236124008007
Alissa:2021:CEC A Neural Approach to Generation of Constructive Heuristics
MohamadAlissa.html
KevinSim.html
EmmaHart.html
http___dx.doi.org_10.1109_CEC45853.2021.9504989 http://dx.doi.org/10.1109/CEC45853.2021.9504989
Aliwi:2020:SIU Firefly Programming For Symbolic Regression Problems
MohamedAliwi.html
SelcukAslan.html
SercanDemirci.html
http___dx.doi.org_10.1109_SIU49456.2020.9302201 http://dx.doi.org/10.1109/SIU49456.2020.9302201
Alizadeh:2011:EAIS Kernel evolution for support vector classification
MehrdadAlizadeh.html
MohammadMehdiEbadzadeh.html
http___dx.doi.org_10.1109_EAIS.2011.5945924 http://dx.doi.org/10.1109/EAIS.2011.5945924
Aljahdali:2010:AICCSA Software effort estimation by tuning COOCMO model parameters using differential evolution
SultanAljahdali.html
AlaaSheta.html
http___dx.doi.org_10.1109_AICCSA.2010.5586985 http://dx.doi.org/10.1109/AICCSA.2010.5586985
Aljahdali:2013:IJARAI Evolving Software Effort Estimation Models Using Multigene Symbolic Regression Genetic Programming
SultanAljahdali.html
AlaaSheta.html
http___thesai.org_Downloads_IJARAI_Volume2No12_Paper_7-Evolving_Software_Effort_Estimation_Models_Using.pdf http://thesai.org/Downloads/IJARAI/Volume2No12/Paper_7-Evolving_Software_Effort_Estimation_Models_Using.pdf
http___dx.doi.org_10.14569_IJARAI.2013.021207 http://dx.doi.org/10.14569/IJARAI.2013.021207
Aljero:2020:ICOASE Hate Speech Detection Using Genetic Programming
MonaKhalifaAAljero.html
NazifeDimililer.html
http___dx.doi.org_10.1109_ICOASE51841.2020.9436621 http://dx.doi.org/10.1109/ICOASE51841.2020.9436621
Aljero:2021:A Genetic Programming Approach to Detect Hate Speech in Social Media
MonaKhalifaAAljero.html
NazifeDimililer.html
http___dx.doi.org_10.1109_ACCESS.2021.3104535 http://dx.doi.org/10.1109/ACCESS.2021.3104535
DBLP:journals/elektrik/AljeroD23 Binary text classification using genetic programming with crossover-based oversampling for imbalanced datasets
MonaKhalifaAAljero.html
NazifeDimililer.html
https___doi.org_10.55730_1300-0632.3978 https://doi.org/10.55730/1300-0632.3978
http___dx.doi.org_10.55730_1300-0632.3978 http://dx.doi.org/10.55730/1300-0632.3978
https___dblp.org_rec_journals_elektrik_AljeroD23.bib https://dblp.org/rec/journals/elektrik/AljeroD23.bib
Alkhaldi:2022:IEEEAccess Ensemble Optimization for Invasive Ductal Carcinoma (IDC) Classification Using Differential Cartesian Genetic Programming
EidAlkhaldi.html
EzzatollahSalari.html
http___dx.doi.org_10.1109_ACCESS.2022.3228176 http://dx.doi.org/10.1109/ACCESS.2022.3228176
Alkroosh:thesis Modelling pile capacity and load-settlement behaviour of piles embedded in sand \& mixed soils using artificial intelligence
IyadSalimJaborAlkroosh.html
http___espace.library.curtin.edu.au_Modelling.pdf http://espace.library.curtin.edu.au/Modelling.pdf
http___hdl.handle.net_20.500.11937_351 http://hdl.handle.net/20.500.11937/351
https___espace.curtin.edu.au_handle_20.500.11937_351 https://espace.curtin.edu.au/handle/20.500.11937/351
Alkroosh:book Modelling pile capacity \& load-settlement behaviour from CPT data: For piles in sand and mixed soils using artificial intelligence
IyadSalimJaborAlkroosh.html
https___www.amazon.co.uk_Modelling-pile-capacity-load-settlement-behaviour_dp_3848436906 https://www.amazon.co.uk/Modelling-pile-capacity-load-settlement-behaviour/dp/3848436906
Alkroosh:2014:SF Predicting pile dynamic capacity via application of an evolutionary algorithm
IyadSalimJaborAlkroosh.html
HNikraz.html
http___dx.doi.org_10.1016_j.sandf.2014.02.013 http://dx.doi.org/10.1016/j.sandf.2014.02.013
http___www.sciencedirect.com_science_article_pii_S0038080614000213 http://www.sciencedirect.com/science/article/pii/S0038080614000213
Allen:2003:NB High-throughput classification of yeast mutants for functional genomics using metabolic footprinting
JessAllen.html
HazelMDavey.html
DavidIBroadhurst.html
JimKHeald.html
JemJRowland.html
StephenGOliver.html
DouglasBKell.html
http___dbkgroup.org_Papers_NatureBiotechnology21_692-696_.pdf http://dbkgroup.org/Papers/NatureBiotechnology21(692-696).pdf
http___dx.doi.org_10.1038_nbt823 http://dx.doi.org/10.1038/nbt823
Allen:2004:AEM Discrimination of Modes of Action of Antifungal Substances by Use of Metabolic Footprinting
JessAllen.html
HazelMDavey.html
DavidIBroadhurst.html
JemJRowland.html
StephenGOliver.html
DouglasBKell.html
http___dx.doi.org_10.1128_AEM.70.10.6157-6165.2004 http://dx.doi.org/10.1128/AEM.70.10.6157-6165.2004
DBLP:conf/gecco/AllenBHK09 Evolving reusable 3D packing heuristics with genetic programming
SamDAllen.html
EdmundBurke.html
MatthewRHyde.html
GrahamKendall.html
http___dx.doi.org_10.1145_1569901.1570029 http://dx.doi.org/10.1145/1569901.1570029
Allen:thesis Algorithms and data structures for three-dimensional packing
SamDAllen.html
http___etheses.nottingham.ac.uk_2779_1_thesis_nicer.pdf http://etheses.nottingham.ac.uk/2779/1/thesis_nicer.pdf
Almal:2005:GPTP Content Diversity in Genetic Programming and its Correlation with Fitness
ArpitAAlmal.html
WilliamPWorzel.html
EricAWollesen.html
DuncanMacLean.html
http___dx.doi.org_10.1007_0-387-28111-8_12 http://dx.doi.org/10.1007/0-387-28111-8_12
1144040 Using genetic programming to classify node positive patients in bladder cancer
ArpitAAlmal.html
AnirbanPMitra.html
RamHDatar.html
PeterFLenehan.html
DavidWFry.html
RichardJCote.html
WilliamPWorzel.html
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http___dx.doi.org_10.1145_1143997.1144040 http://dx.doi.org/10.1145/1143997.1144040
Almal:2007:GPTP Program Structure-Fitness Disconnect and Its Impact On Evolution In GP
ArpitAAlmal.html
DuncanMacLean.html
WilliamPWorzel.html
http___dx.doi.org_10.1007_978-0-387-76308-8_9 http://dx.doi.org/10.1007/978-0-387-76308-8_9
Almal:2008:GPTP A Population Based Study of Evolutionary Dynamics in Genetic Programming
ArpitAAlmal.html
DuncanMacLean.html
WilliamPWorzel.html
http___dx.doi.org_10.1007_978-0-387-87623-8_2 http://dx.doi.org/10.1007/978-0-387-87623-8_2
almarimi2020community On the Detection of Community Smells using Genetic Programming-based Ensemble Classifier Chain
NuriAlmarimi.html
AliOuni.html
MoatazChouchen.html
IslemSaidani.html
MohamedWiemMkaouer.html
https___conf.researchr.org_details_icgse-2020_icgse-2020-research-papers_6_On-the-Detection-of-Community-Smells-using-Genetic-Programming-based-Ensemble-Classif https://conf.researchr.org/details/icgse-2020/icgse-2020-research-papers/6/On-the-Detection-of-Community-Smells-using-Genetic-Programming-based-Ensemble-Classif
http___dx.doi.org_10.1145_3372787.3390439 http://dx.doi.org/10.1145/3372787.3390439
https___github.com_GP-ECC_community-smells https://github.com/GP-ECC/community-smells
Almarimi:2020:ICGSE On the Detection of Community Smells Using Genetic Programming-based Ensemble Classifier Chain
NuriAlmarimi.html
AliOuni.html
MoatazChouchen.html
IslemSaidani.html
MohamedWiemMkaouer.html
http___ieeexplore.ieee.org_xpl_articleDetails.jsp_tp__arnumber_10148849 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=10148849
alMasalma:2022:GECCOcomp Genetic Programming with External Memory in Sequence Recall Tasks
MihyarAlMasalma.html
MalcolmHeywood.html
http___dx.doi.org_10.1145_3520304.3528883 http://dx.doi.org/10.1145/3520304.3528883
https___vimeo.com_723511528 https://vimeo.com/723511528
alMasalma:2023:GPEM Benchmarking ensemble genetic programming with a linked list external memory on scalable partially observable tasks
MihyarAlMasalma.html
MalcolmHeywood.html
https___rdcu.be_daFLX https://rdcu.be/daFLX
http___dx.doi.org_10.1007_s10710-022-09446-8 http://dx.doi.org/10.1007/s10710-022-09446-8
Almeida:2017:ieeeGRSL Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions
AlexandreEAlmeida.html
RicardodaSilvaTorres.html
http___dx.doi.org_10.1109_LGRS.2017.2719033 http://dx.doi.org/10.1109/LGRS.2017.2719033
Almeida:2015:EI Deriving vegetation indices for phenology analysis using genetic programming
JurandyGAlmeidaJr.html
JeferssonAlexdosSantos.html
WanerOMiranda.html
BrunadeCostaAlberton.html
LeonorPatriciaCerdeiraMorellato.html
RicardodaSilvaTorres.html
http___dx.doi.org_10.1016_j.ecoinf.2015.01.003 http://dx.doi.org/10.1016/j.ecoinf.2015.01.003
http___www.sciencedirect.com_science_article_pii_S1574954115000114 http://www.sciencedirect.com/science/article/pii/S1574954115000114
Almeida:2016:SIBGRAPI A Genetically Programmable Hybrid Virtual Reconfigurable Architecture for Image Filtering Applications
MAAlmeida.html
EmersonCarlosPedrino.html
MariadoCarmoNicoletti.html
http___dx.doi.org_10.1109_SIBGRAPI.2016.029 http://dx.doi.org/10.1109/SIBGRAPI.2016.029
Almeida:2018:ICAE Hybrid Evolvable Hardware for automatic generation of image filters
MAAlmeida.html
EmersonCarlosPedrino.html
http___dx.doi.org_10.3233_ICA-180561 http://dx.doi.org/10.3233/ICA-180561
almgren:2000:CADGP Communicating Agents Developed with Genetic Programming
MagnusAlmgren.html
AlMosawe:thesis Investigation of the performance of cracked steel members strengthened with carbon fibre reinforced polymers under impact loads
AlaaAl-Mosawe.html
http___hdl.handle.net_1959.3_414765 http://hdl.handle.net/1959.3/414765
https___researchbank.swinburne.edu.au_file_0cca0b0c-0219-4cb0-b0d3-b33bcdb159a8_1_Alaa_20Al-Mosawe_20Thesis.pdf https://researchbank.swinburne.edu.au/file/0cca0b0c-0219-4cb0-b0d3-b33bcdb159a8/1/Alaa%20Al-Mosawe%20Thesis.pdf
AlMosawe:2017:CS Strength of Cfrp-steel double strap joints under impact loads using genetic programming
AlaaAl-Mosawe.html
RobinKalfat.html
RiadhAl-Mahaidi.html
http___www.sciencedirect.com_science_article_pii_S0263822316317767 http://www.sciencedirect.com/science/article/pii/S0263822316317767
http___dx.doi.org_10.1016_j.compstruct.2016.11.016 http://dx.doi.org/10.1016/j.compstruct.2016.11.016
Al-Mulla:2009:EMBC Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction
MohammadRAl-Mulla.html
FranciscoSepulveda.html
MartinColley.html
AhmedKattan.html
http___dx.doi.org_10.1109_IEMBS.2009.5335368 http://dx.doi.org/10.1109/IEMBS.2009.5335368
AlNajar:2022:GI Genetic Improvement of Shoreline Evolution Forecasting Models
MahmoudAlNajar.html
RafaelAlmar.html
ErwinWJBergsma.html
Jean-MarcDelvit.html
DennisGWilson.html
http___www.cs.ucl.ac.uk_staff_W.Langdon_gecco2022_gi2022_papers_AlNajar_2022_GI.pdf http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/AlNajar_2022_GI.pdf
http___dx.doi.org_10.1145_3520304.3534041 http://dx.doi.org/10.1145/3520304.3534041
https___sourcesup.renater.fr_wiki_atelieromp__media_presentation_envia_mahmoud_al_ajar_omp_v2.pdf https://sourcesup.renater.fr/wiki/atelieromp/_media/presentation_envia_mahmoud_al_ajar_omp_v2.pdf
http___geneticimprovementofsoftware.com_slides_gi2022gecco_najar-genetic-improvement-of-shoreline-evolution-forecasting-gi-gecco-22.pdf http://geneticimprovementofsoftware.com/slides/gi2022gecco/najar-genetic-improvement-of-shoreline-evolution-forecasting-gi-gecco-22.pdf
https___www.youtube.com_watch_v_66UiDk9lsnc_list_PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje_index_6 https://www.youtube.com/watch?v=66UiDk9lsnc&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=6
alnajar2023improving Improving a Shoreline Forecasting Model with Symbolic Regression
MahmoudAlNajar.html
RafaelAlmar.html
ErwinWJBergsma.html
Jean-MarcDelvit.html
DennisGWilson.html
https___www.climatechange.ai_papers_iclr2023_21 https://www.climatechange.ai/papers/iclr2023/21
https___www.climatechange.ai_papers_iclr2023_21_paper.pdf https://www.climatechange.ai/papers/iclr2023/21/paper.pdf
https___hal.science_hal-04281530 https://hal.science/hal-04281530
AlNajar:thesis Estimating Coastal Evolution with Machine Learning
MahmoudAlNajar.html
https___www.isae-supaero.fr_IMG_pdf_annonce_soutenance_these_m_al_najar.pdf https://www.isae-supaero.fr/IMG/pdf/annonce_soutenance_these_m_al_najar.pdf
https___zoom.us_my_dennisgwilson https://zoom.us/my/dennisgwilson
ALOISIO:2023:ymssp Physics-based models, surrogate models and experimental assessment of the vehicle-bridge interaction in braking conditions
AngeloAloisio.html
AlessandroContento.html
RoccoAlaggio.html
GiuseppeQuaranta.html
http___dx.doi.org_10.1016_j.ymssp.2023.110276 http://dx.doi.org/10.1016/j.ymssp.2023.110276
https___www.sciencedirect.com_science_article_pii_S0888327023001838 https://www.sciencedirect.com/science/article/pii/S0888327023001838
Alonso:2008:ieeeICTAI Straight Line Programs: A New Linear Genetic Programming Approach
CesarLuisAlonso.html
JorgePuentePeinador.html
JoseLuisMontanaArnaiz.html
http___dx.doi.org_10.1109_ICTAI.2008.14 http://dx.doi.org/10.1109/ICTAI.2008.14
Alonso:2009:IJAIT A new Linear Genetic Programming approach based on straight line programs: some Theoretical and Experimental Aspects
CesarLuisAlonso.html
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http___hdl.handle.net_10063_8898 http://hdl.handle.net/10063/8898
https___researcharchive.vuw.ac.nz_xmlui_bitstream_handle_10063_8898_thesis_access.pdf https://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/8898/thesis_access.pdf
Azaria:2016:GPTP Evolving Artificial General Intelligence for Video Controllers
ItayAzaria.html
AchiyaElyasaf.html
MosheSipper.html
https___www.cs.bgu.ac.il__sipper_publications_Evolving_20Artificial_20General_20Intelligence.pdf https://www.cs.bgu.ac.il/~sipper/publications/Evolving%20Artificial%20General%20Intelligence.pdf
https___www.springer.com_us_book_9783319970875 https://www.springer.com/us/book/9783319970875
http___dx.doi.org_10.1007_978-3-319-97088-2_4 http://dx.doi.org/10.1007/978-3-319-97088-2_4
eurogp:AzariaS05 GP-Gammon: Using Genetic Programming to Evolve Backgammon Players
YanivAzaria.html
MosheSipper.html
http___dx.doi.org_10.1007_978-3-540-31989-4_12 http://dx.doi.org/10.1007/978-3-540-31989-4_12
http___dx.doi.org_10.1007_b107383 http://dx.doi.org/10.1007/b107383
azaria:2005:GPEM GP-Gammon: Genetically Programming Backgammon Players
YanivAzaria.html
MosheSipper.html
http___www.cs.bgu.ac.il__sipper_papabs_gpgammon.pdf http://www.cs.bgu.ac.il/~sipper/papabs/gpgammon.pdf
https___rdcu.be_c7iTQ https://rdcu.be/c7iTQ
http___dx.doi.org_10.1007_s10710-005-2990-0 http://dx.doi.org/10.1007/s10710-005-2990-0
Azevedo:2016:CSCI Genetic Programming in Geostatistical Reservoir Geophysics
LeonardoAzevedo.html
RubenNunes.html
AmilcarSoares.html
http___dx.doi.org_10.1109_CSCI.2016.0228 http://dx.doi.org/10.1109/CSCI.2016.0228
Azimlu:2019:ICCSE Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models
FatemeAzimlu.html
ShahryarRahnamayan.html
MasoudMakrehchi.html
NaveenKalra.html
http___dx.doi.org_10.1109_ICCSE.2019.8845381 http://dx.doi.org/10.1109/ICCSE.2019.8845381
Azimlu:2021:RWACMO House Price Prediction Using Clustering and Genetic Programming along with Conducting a Comparative Study
FatemeAzimlu.html
ShahryarRahnamayan.html
MasoudMakrehchi.html
http___dx.doi.org_10.1145_3449726.3463141 http://dx.doi.org/10.1145/3449726.3463141
Aziz:2016:EuroGP Search-Based SQL Injection Attacks Testing using Genetic Programming
BenjaminAziz.html
MohamedBahyBader-El-Den.html
CeranaHippolyte.html
http___dx.doi.org_10.1007_978-3-319-30668-1_12 http://dx.doi.org/10.1007/978-3-319-30668-1_12
Azmi:2020:Morgeo Generate knowledge base from very high spatial resolution satellite image using robust classification rules and genetic programming
RidaAzmi.html
HichamAmar.html
AbderrahimNorelyaqine.html
http___dx.doi.org_10.1109_Morgeo49228.2020.9121914 http://dx.doi.org/10.1109/Morgeo49228.2020.9121914
Azzali:2019:EuroGP A Vectorial Approach to Genetic Programming
IreneAzzali.html
LeonardoVanneschi.html
SaraSilva.html
IllyaBakurov.html
MarioGiacobini.html
https___hdl.handle.net_2318_1725688 https://hdl.handle.net/2318/1725688
https___iris.unito.it_retrieve_e27ce42f-33ca-2581-e053-d805fe0acbaa_Azzali.pdf https://iris.unito.it/retrieve/e27ce42f-33ca-2581-e053-d805fe0acbaa/Azzali.pdf
https___www.springer.com_us_book_9783030166694 https://www.springer.com/us/book/9783030166694
http___dx.doi.org_10.1007_978-3-030-16670-0_14 http://dx.doi.org/10.1007/978-3-030-16670-0_14
Azzali:GPEM Towards the use of genetic programming in the ecological modelling of mosquito population dynamics
IreneAzzali.html
LeonardoVanneschi.html
AndreaMosca.html
LuigiBertolotti.html
MarioGiacobini.html
https___iris.unito.it_retrieve_handle_2318_1722575_562795_Manuscript.pdf https://iris.unito.it/retrieve/handle/2318/1722575/562795/Manuscript.pdf
https___rdcu.be_cQCew https://rdcu.be/cQCew
http___dx.doi.org_10.1007_s10710-019-09374-0 http://dx.doi.org/10.1007/s10710-019-09374-0
Azzali:2020:EuroGP Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
IreneAzzali.html
LeonardoVanneschi.html
MarioGiacobini.html
http___dx.doi.org_10.1007_978-3-030-44094-7_4 http://dx.doi.org/10.1007/978-3-030-44094-7_4
AZZALI:2020:ASC Towards the use of vector based GP to predict physiological time series
IreneAzzali.html
LeonardoVanneschi.html
IllyaBakurov.html
SaraSilva.html
MarcoIvaldi.html
MarioGiacobini.html
http___dx.doi.org_10.1016_j.asoc.2020.106097 http://dx.doi.org/10.1016/j.asoc.2020.106097
http___www.sciencedirect.com_science_article_pii_S1568494620300375 http://www.sciencedirect.com/science/article/pii/S1568494620300375
Azzali:2022:evoapplications Vectorial GP for Alzheimer's Disease Prediction Through Handwriting Analysis
IreneAzzali.html
NicoleDaliaCilia.html
ClaudioDeStefano.html
FrancescoRFontanella.html
MarioGiacobini.html
LeonardoVanneschi.html
http___dx.doi.org_10.1007_978-3-031-02462-7_33 http://dx.doi.org/10.1007/978-3-031-02462-7_33
AZZALI:2024:swevo Automatic feature extraction with Vectorial Genetic Programming for Alzheimer's Disease prediction through handwriting analysis
IreneAzzali.html
NicoleDaliaCilia.html
ClaudioDeStefano.html
FrancescoRFontanella.html
MarioGiacobini.html
LeonardoVanneschi.html
http___dx.doi.org_10.1016_j.swevo.2024.101571 http://dx.doi.org/10.1016/j.swevo.2024.101571
https___www.sciencedirect.com_science_article_pii_S2210650224001093 https://www.sciencedirect.com/science/article/pii/S2210650224001093
conf/adma/AzzawiHAXAA17 Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets
HasseebAzzawi.html
JingyuHou.html
RussulAlanni.html
YongXiang.html
RanaAbdu-Aljabar.html
AliAzzawi.html
http___dx.doi.org_10.1007_978-3-319-69179-4_38 http://dx.doi.org/10.1007/978-3-319-69179-4_38
Azzini:2011:IA Evolutionary ANNs: A state of the art survey
AntoniaAzzini.html
AndreaGBTettamanzi.html
http___dx.doi.org_10.3233_IA-2011-0002 http://dx.doi.org/10.3233/IA-2011-0002
B:2021:ICIRCA Comparison of Conventional and Automated Machine Learning approaches for Breast Cancer Prediction
AkaramuthalviJB.html
SujaPalaniswamy.html
http___dx.doi.org_10.1109_ICIRCA51532.2021.9544863 http://dx.doi.org/10.1109/ICIRCA51532.2021.9544863
Baareh:2018:IJACSA Evolutionary Design of a Carbon Dioxide Emission Prediction Model using Genetic Programming
AbdelKarimBaareh.html
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Baars:2011:FedCSIS Search-based testing, the underlying engine of Future Internet testing
ArthurIBaars.html
KiranLakhotia.html
TanjaEJVos.html
JoachimWegener.html
http___ieeexplore.ieee.org_stamp_stamp.jsp_tp__arnumber_6078178 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6078178
Babaelahi:2016:Energy Analytical closed-form model for predicting the power and efficiency of Stirling engines based on a comprehensive numerical model and the genetic programming
MojtabaBabaelahi.html
HoseynSayyaadi.html
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BABAELAHI:2017:EML Optimum analytical design of medical heat sink with convex parabolic fin including variable thermal conductivity and mass transfer
MojtabaBabaelahi.html
HamedEshraghi.html
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http___www.sciencedirect.com_science_article_pii_S2352431616302826 http://www.sciencedirect.com/science/article/pii/S2352431616302826
BABAELAHI:2020:SETA Analytical design and optimization of a new hybrid solar-driven micro gas turbine/stirling engine, based on exergo-enviro-economic concept
MojtabaBabaelahi.html
HamedJafari.html
http___dx.doi.org_10.1016_j.seta.2020.100845 http://dx.doi.org/10.1016/j.seta.2020.100845
https___www.sciencedirect.com_science_article_pii_S2213138820312728 https://www.sciencedirect.com/science/article/pii/S2213138820312728
Babanajad:2013:AiC Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming
SaeedKBabanajad.html
AHGandomi.html
DMohammadzadehShadmehri.html
AHAlavi.html
http___www.sciencedirect.com_science_article_pii_S0926580513001301 http://www.sciencedirect.com/science/article/pii/S0926580513001301
http___dx.doi.org_10.1016_j.autcon.2013.08.016 http://dx.doi.org/10.1016/j.autcon.2013.08.016
Babanajad:2015:hbgpa Application of Genetic Programming for Uniaxial and Multiaxial Modeling of Concrete
SaeedKBabanajad.html
http___dx.doi.org_10.1007_978-3-319-20883-1_16 http://dx.doi.org/10.1007/978-3-319-20883-1_16
Babanajad:thesis Methods for Sensing, Analysis and Computation of Loads and Distributed Damage in Bridges
SaeedKBabanajad.html
https___dspace-prod.lib.uic.edu_bitstream_handle_10027_20220_Karimbabanajadmamaghani_Saeed.pdf https://dspace-prod.lib.uic.edu/bitstream/handle/10027/20220/Karimbabanajadmamaghani_Saeed.pdf
https___indigo.uic.edu_handle_10027_20220 https://indigo.uic.edu/handle/10027/20220
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http___hdl.handle.net_10027_20220 http://hdl.handle.net/10027/20220
Babanajad:2017:AES New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach
SaeedKBabanajad.html
AHGandomi.html
AHAlavi.html
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Babar:2013:ICDIM Genetic Programming Based Degree Constrained Spanning Tree Extraction
ZaheerBabar.html
MuhammadWaqas.html
ZahidHalim.html
MuhammadArshadIslam.html
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Baber:2009:MSG Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work Analysis and Social Network Analysis
ChrisBaber.html
NevilleStanton.html
DanielHoward.html
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baber:2002:EuroGP Evolutionary Algorithm Approach to Bilateral Negotiations
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KrishnaKummamuru.html
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babic:2014:etv Using of genetic programming in engineering
MatejBabic.html
PeterKokol.html
IgorBelic.html
PeterPanjan.html
MihaKovacic.html
JozeBalic.html
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Babic:2021:Remote_Sensing Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans
MatejBabic.html
DusanPetrovic.html
JostSodnik.html
BozoSoldo.html
MarkoKomac.html
OlenaChernieva.html
MihaKovacic.html
MatjazMikos.html
MicheleCali.html
https___repozitorij.uni-lj.si_IzpisGradiva.php_id_127268 https://repozitorij.uni-lj.si/IzpisGradiva.php?id=127268
https___www.mdpi.com_2072-4292_13_9_1711 https://www.mdpi.com/2072-4292/13/9/1711
http___dx.doi.org_10.3390_rs13091711 http://dx.doi.org/10.3390/rs13091711
Babic:2021:SpliTech A New Composite Method of Modeling Bicycle Traffic using Convolutional Neural Networks and Genetic programming
MatejBabic.html
BrankoSter.html
JanezPovh.html
JoelJPCRodrigues.html
http___dx.doi.org_10.23919_SpliTech52315.2021.9566405 http://dx.doi.org/10.23919/SpliTech52315.2021.9566405
BABIC:2021:PM Evaluation of microstructural complex geometry of robot laser hardened materials through a genetic programming model
MatejBabic.html
GLesiuk.html
DraganMarinkovic.html
MicheleCali.html
http___dx.doi.org_10.1016_j.promfg.2021.10.036 http://dx.doi.org/10.1016/j.promfg.2021.10.036
https___www.sciencedirect.com_science_article_pii_S235197892100233X https://www.sciencedirect.com/science/article/pii/S235197892100233X
babic:2022:AS Complexity Modeling of Steel-Laser-Hardened Surface Microstructures
MatejBabic.html
DraganMarinkovic.html
MarcoBonfanti.html
MicheleCali.html
https___www.mdpi.com_2076-3417_12_5_2458 https://www.mdpi.com/2076-3417/12/5/2458
http___dx.doi.org_10.3390_app12052458 http://dx.doi.org/10.3390/app12052458
Babic:2022:FF A New Method of Quantifying the Complexity of Fractal Networks
MatejBabic.html
DraganMarinkovic.html
MihaKovacic.html
BrankoSter.html
MicheleCali.html
https___www.mdpi.com_2504-3110_6_6_282 https://www.mdpi.com/2504-3110/6/6/282
http___dx.doi.org_10.3390_fractalfract6060282 http://dx.doi.org/10.3390/fractalfract6060282
babic:2023:FaF A New Approach to Determining the Network Fractality with Application to Robot-Laser-Hardened Surfaces of Materials
MatejBabic.html
DraganMarinkovic.html
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http___dx.doi.org_10.3390_fractalfract7100710 http://dx.doi.org/10.3390/fractalfract7100710
Babic:2024:sv-jme Selective Laser Melting: A Novel Method for Surface Roughness Analysis
MatejBabic.html
MihaKovacic.html
CristianoFragassa.html
RomanSturm.html
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http___dx.doi.org_10.5545_sv-jme.2024.1009 http://dx.doi.org/10.5545/sv-jme.2024.1009
babovic:1994:camh Use of computational adaptive methodologies in hydroinformatics
VladanBabovic.html
AnthonyWMinns.html
http___www.amazon.co.uk_Hydroinformatics-Proceedings-International-Conference-Netherlands_dp_9054105127 http://www.amazon.co.uk/Hydroinformatics-Proceedings-International-Conference-Netherlands/dp/9054105127
babovic:1995:gmibed Genetic Model Induction Based on Experimental Data
VladanBabovic.html
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babovic:thesis Emergence, Evolution, Intelligence: Hydroinformatics
VladanBabovic.html
http___repository.tudelft.nl_view_ir_uuid_3A58c50efe-4a6a-40b4-8c60-2b81d629b49c_ http://repository.tudelft.nl/view/ir/uuid%3A58c50efe-4a6a-40b4-8c60-2b81d629b49c/
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babovic:book Emergence, evolution, intelligence; Hydroinformatics - A study of distributed and decentralised computing using intelligent agents
VladanBabovic.html
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babovic:1996:wmbAI Can water resources management benefit from artificial intelligence?
VladanBabovic.html
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babovic:1997:eehd1 The evolution of equation from hydraulic data, Part I: Theory
VladanBabovic.html
MichaelBAbbott.html
http___dx.doi.org_10.1080_00221689709498420 http://dx.doi.org/10.1080/00221689709498420
babovic:1997:eehd2 The evolution of equation from hydraulic data, Part II: Applications
VladanBabovic.html
MichaelBAbbott.html
http___dx.doi.org_10.1080_00221689709498421 http://dx.doi.org/10.1080/00221689709498421
babovic:1997:mfnls On the Modelling and Forecasting of Non-linear Systems
VladanBabovic.html
http___www.amazon.co.uk_gp_search_index_books_linkCode_qs_keywords_9054108975 http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=9054108975
babovic:1998:stdlkm Sediment transport data - Large knowledge mine
VladanBabovic.html
babovic:1998:dmtsmf A data mining approach to time series modelling and forecasting
VladanBabovic.html
babovic:1998:mstGP Mining sediment transport data with genetic programming
VladanBabovic.html
babovic:1999:cskd-veg Computer supported knowledge discovery - A case study in flow resistance induced by vegetation
VladanBabovic.html
MaartenKeijzer.html
http___www.iahr.org_membersonly_grazproceedings99_pdf_C021.pdf http://www.iahr.org/membersonly/grazproceedings99/pdf/C021.pdf
babovic:1999:d2k Data to knowledge - The new scientific paradigm
VladanBabovic.html
MaartenKeijzer.html
http___bookweb.kinokuniya.co.jp_htmy_0863802486.html http://bookweb.kinokuniya.co.jp/htmy/0863802486.html
me15 Evolutionary algorithms approach to induction of differential equations
VladanBabovic.html
MaartenKeijzer.html
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babovic:1999:td2ksed Data Mining and Knowledge Discovery in Sediment Transport
VladanBabovic.html
http___dx.doi.org_10.1111_0885-9507.00202 http://dx.doi.org/10.1111/0885-9507.00202
babovic:1999:GPmie Genetic programming as a model induction engine
VladanBabovic.html
MaartenKeijzer.html
http___jh.iwaponline.com_content_2_1_35 http://jh.iwaponline.com/content/2/1/35
http___dx.doi.org_10.2166_hydro.2000.0004 http://dx.doi.org/10.2166/hydro.2000.0004
Babovic:2000:IAHR On Computer-Aided Discovery of Knowledge in Hydraulic Engineering
VladanBabovic.html
HBergmann.html
me25 On the introduction of declarative bias in knowledge discovery computer systems
VladanBabovic.html
MaartenKeijzer.html
me27 An evolutionary approach to knowledge induction: Genetic Programming in Hydraulic Engineering
VladanBabovic.html
MaartenKeijzer.html
DavidRodriguez-Aguilera.html
JoeHarrington.html
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http___dx.doi.org_10.1061_40569_2001_64 http://dx.doi.org/10.1061/40569(2001)64
me24 Modelling of water supply assets: a data mining approach
VladanBabovic.html
Jean-PhilippeDrecourt.html
MaartenKeijzer.html
PeterFriisHansen.html
http___www.sciencedirect.com_science_article_B6VR2-4718F0J-1_2_e361659261f99d438f8f2207f67eedf8 http://www.sciencedirect.com/science/article/B6VR2-4718F0J-1/2/e361659261f99d438f8f2207f67eedf8
NordicHy Rainfall Runoff Modelling based on Genetic Programming
VladanBabovic.html
MaartenKeijzer.html
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Babovic:2005:HP Data mining in hydrology
VladanBabovic.html
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Babovic:2006: Rainfall-Runoff Modeling Based on Genetic Programming
VladanBabovic.html
MaartenKeijzer.html
http___onlinelibrary.wiley.com_doi_10.1002_0470848944.hsa017_abstract http://onlinelibrary.wiley.com/doi/10.1002/0470848944.hsa017/abstract
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Babovic:2007:NMHS Data-Driven Knowledge Discovery: Four Roads to Vegetation-Induced Roughness Formulae
VladanBabovic.html
http___www.amazon.com_Numerical-Modelling-Hydrodynamics-Water-Resources_dp_0415440564_ref_cm_cr_pr_pb_t http://www.amazon.com/Numerical-Modelling-Hydrodynamics-Water-Resources/dp/0415440564/ref=cm_cr_pr_pb_t
Babovic:2009:JH Introducing knowledge into learning based on genetic programming
VladanBabovic.html
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Babovic:2010:ECinH Evolutionary Computing in Hydrology
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KRaoRaghuraj.html
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Babu:2007:EL Genetic Programming for Symbolic Regression of Chemical Process Systems
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Babu:2016:ICCES Approximation of digital circuits using cartesian genetic programming
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Babu:2023:ICAEECI OCR-Based Multi-class Classification of Hate Speech in Images
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Babuska:2019:GECCO Genetic programming methods for reinforcement learning
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Bach:2019:ICEAA Evolved Design of Microstrip Patch Antenna by Genetic Programming
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LinhHoManh.html
KiemNguyenKhac.html
MicheleBeccaria.html
AndreaMassaccesi.html
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http___dx.doi.org_10.1109_ICEAA.2019.8879155 http://dx.doi.org/10.1109/ICEAA.2019.8879155
back:1997:survey Evolutionary computation: comments on the history and current state
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back:2000:EC1 Mutation operators
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DavidBFogel.html
LDarrellWhitley.html
PeterJohnAngeline.html
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Back:2004:UPP Inverse Design of Cellular Automata by Genetic Algorithms: An Unconventional Programming Paradigm
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LarsWillmes.html
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Baeck:2023:GPTP Automatic Algorithm Configuration for Expensive Optimization Tasks
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backer:1996:WSC Learning with missing data using Genetic Programming
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conf/evoW/BackmanD08 A Generative Representation for the Evolution of Jazz Solos
KjellBackman.html
PalleDahlstedt.html
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Badan:2019:TPNC Optimizing Convolutional Neural Networks for Embedded Systems by Means of Neuroevolution
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LukasSekanina.html
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RiccardoPoli.html
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bader-el-den07:_gener_sat_local_searc_heuris Generating SAT Local-Search Heuristics using a GP Hyper-Heuristic Framework
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RiccardoPoli.html
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Bader-El-Den:2008:evocop Inc*: An Incremental Approach for Improving Local Search Heuristics
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Bader-El-Den:2008:WCCI Analysis and Extension of the Inc* on the Satisfiability Testing Problem
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RiccardoPoli.html
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Bader-El-Den:2008:GPTP Evolving Effective Incremental Solvers for SAT with a Hyper-Heuristic Framework Based on Genetic Programming
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RiccardoPoli.html
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Bader-El-Den:2008:gecco Evolving Heuristics with Genetic Programming
MohamedBahyBader-El-Den.html
RiccardoPoli.html
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BaderElDen:2009:cec Grammar-Based Genetic Programming for Timetabling
MohamedBahyBader-El-Den.html
RiccardoPoli.html
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Bader-El-Den:thesis Investigation of the role of Genetic Programming in a Hyper-Heuristic Framework for Combinatorial Optimization Problems
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conf/ijcci/Bader-El-DenF09 Evolving Effective Bidding Functions for Auction based Resource Allocation Framework
MohamedBahyBader-El-Den.html
ShaheenFatima.html
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journals/memetic/Bader-El-DenPF09 Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework
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RiccardoPoli.html
ShaheenFatima.html
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Bader-El-Den:2010:EuroGP Genetic Programming for Auction Based Scheduling
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ShaheenFatima.html
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1277272 The roles of diversity preservation and mutation in preventing population collapse in multiobjective genetic programming
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PeterIRockett.html
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PeterIRockett.html
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PeterIRockett.html
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Badran:2017:IJCA Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection
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Badura:2014:ELEKTRO Bimodal vowel recognition using fuzzy logic networks - naive approach
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OndrejSkvarek.html
MartinKlimo.html
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journals/soco/BaeJKKKHM10 Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling
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Baele:2009:cec Open-Ended On-Board Evolutionary Robotics for Robot Swarms
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Baeta:2021:evoapplications TensorGP - Genetic Programming Engine in TensorFlow
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Baeta:2021:GECCO Speed Benchmarking of Genetic Programming Frameworks
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baeta:2022:SN Exploring Genetic Programming in TensorFlow with TensorGP
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Bai:2008:ieeeSMI Self-organizing primitives for automated shape composition
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Binard:book Abstraction-Based Genetic Programming: An Application of the polymorphically-typed lambda calculus to genetic programming
FranckJLBinard.html
https___www.amazon.co.uk_Abstraction-Based-Genetic-Programming-Application-polymorphically-typed_dp_3639191730 https://www.amazon.co.uk/Abstraction-Based-Genetic-Programming-Application-polymorphically-typed/dp/3639191730
bingol:2018:NCaA Application of gene expression programming in hot metal forming for intelligent manufacturing
SedatBingol.html
HidirYankiKilicgedik.html
http___link.springer.com_article_10.1007_s00521-016-2718-5 http://link.springer.com/article/10.1007/s00521-016-2718-5
http___dx.doi.org_10.1007_s00521-016-2718-5 http://dx.doi.org/10.1007/s00521-016-2718-5
emse19 A comparison of tree- and line-oriented observational slicing
DavidWBinkley.html
NicolasEGold.html
SyedIslam.html
JensKrinke.html
ShinYoo.html
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http___dx.doi.org_10.1007_s10664-018-9675-9 http://dx.doi.org/10.1007/s10664-018-9675-9
Birchenhall:1995:EJ Genetic Algorithms, Classifier Systems and Genetic Programming and their Use in the Models of Adaptive Behaviour and Learning
ChrisBirchenhall.html
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DBLP:conf/petra/Bird22 EEG Wavelet Classification for Fall Detection with Genetic Programming
JordanJBird.html
https___doi.org_10.1145_3529190.3535339 https://doi.org/10.1145/3529190.3535339
http___dx.doi.org_10.1145_3529190.3535339 http://dx.doi.org/10.1145/3529190.3535339
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Bird:2023:GPEM Fall compensation detection from EEG using neuroevolution and genetic hyperparameter optimisation
JordanJBird.html
AhmadLotfi.html
https___rdcu.be_dcJdp https://rdcu.be/dcJdp
http___dx.doi.org_10.1007_s10710-023-09453-3 http://dx.doi.org/10.1007/s10710-023-09453-3
Bird:thesis Optimizing Resource Allocations for Dynamic Interactive Applications
SarahBird.html
https___aspire.eecs.berkeley.edu_publication_optimizing-resource-allocations-for-dynamic-interactive-applications_ https://aspire.eecs.berkeley.edu/publication/optimizing-resource-allocations-for-dynamic-interactive-applications/
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oai:CiteSeerPSU:397549 Schemas and Genetic Programming
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http___dx.doi.org_10.1007_978-94-010-0870-9_50 http://dx.doi.org/10.1007/978-94-010-0870-9_50
Birtolo:2010:ICEIS Supporting Menu Layout Design by Genetic Programming
CosimoBirtolo.html
RobertoArmenise.html
LuigiTroiano.html
https___www.poste.it_azienda_research_development_pubblicazioni_ICEIS10_20-_20SUPPORTING_20MENU_20LAYOUT_20DESIGN_20BY_20GP.pdf https://www.poste.it/azienda/research_development/pubblicazioni/ICEIS10%20-%20SUPPORTING%20MENU%20LAYOUT%20DESIGN%20BY%20GP.pdf
bisat:1998:ussbctn Using Adaptive Agents to Study Bilateral Contracts and Trade Networks
MonaTBisat.html
CharlesWRichterJr.html
GeraldBSheble.html
http___dakotarichter.com_papers_gp98PosterPaperBisatRichterSheble.pdf http://dakotarichter.com/papers/gp98PosterPaperBisatRichterSheble.pdf
Bischl:thesis Model and Algorithm Selection in Statistical Learning and Optimization
BerndBischl.html
https___eldorado.tu-dortmund.de_bitstream_2003_32861_1_phd.pdf https://eldorado.tu-dortmund.de/bitstream/2003/32861/1/phd.pdf
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http___dx.doi.org_10.17877_DE290R-7142 http://dx.doi.org/10.17877/DE290R-7142
BISHAYEE:2023:gsd Modeling, optimization and comparative study on abatement of fluoride from synthetic solution using activated laterite soil and fly ash
BhaskarBishayee.html
AbhayKumar.html
SandipKumarLahiri.html
SusmitaDutta.html
BiswajitRuj.html
http___dx.doi.org_10.1016_j.gsd.2023.101016 http://dx.doi.org/10.1016/j.gsd.2023.101016
https___www.sciencedirect.com_science_article_pii_S2352801X23001169 https://www.sciencedirect.com/science/article/pii/S2352801X23001169
conf/evoW/BishopCT14 Feature Construction Using Genetic Programming for Classification of Images by Aesthetic Value
AndrewBishop.html
VictorCiesielski.html
KarenTrist.html
http___dx.doi.org_10.1007_978-3-662-44335-4 http://dx.doi.org/10.1007/978-3-662-44335-4
bishop:2024:GECCO Evolutionary Exploration of Triply Periodic Minimal Surfaces via Quality Diversity
JordanTBishop.html
JasonJooste.html
DavidHoward.html
http___dx.doi.org_10.1145_3638529.3654039 http://dx.doi.org/10.1145/3638529.3654039
Biswas:thesis Optimization of Hydrometallurgical Processing of Lean Manganese Bearing Resources
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http___www.idr.iitkgp.ac.in_xmlui_handle_123456789_951 http://www.idr.iitkgp.ac.in/xmlui/handle/123456789/951
Biswas:2011:MMP Data-Driven Multiobjective Analysis of Manganese Leaching from Low Grade Sources Using Genetic Algorithms, Genetic Programming, and Other Allied Strategies
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OgierMaitre.html
DebangaNandanMondal.html
SyamalKantiDas.html
ProdipKumarSen.html
PierreCollet.html
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conf/softcomp/BittencourtSLAO10 The Gene Expression Programming Applied to Demand Forecast
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SidneySchossland.html
RaulLandmann.html
DenioMurilodeAguiar.html
AdilsonGomesDeOliveira.html
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Bladek:2016:GECCOcomp Simultaneous Synthesis of Multiple Functions using Genetic Programming with Scaffolding
IwoBladek.html
KrzysztofKrawiec.html
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http___dx.doi.org_10.1145_2908961.2908992 http://dx.doi.org/10.1145/2908961.2908992
Bladek:2017:EuroGP Evolutionary Program Sketching
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Bladek:EC Counterexample-Driven Genetic Programming: Heuristic Program Synthesis from Formal Specifications
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KrzysztofKrawiec.html
JerrySwan.html
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Bladek:2019:GECCO Solving symbolic regression problems with formal constraints
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http___dx.doi.org_10.1145_3321707.3321743 http://dx.doi.org/10.1145/3321707.3321743
Bladek:ieeeTEC Counterexample-Driven Genetic Programming for Symbolic Regression with Formal Constraints
IwoBladek.html
KrzysztofKrawiec.html
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Blair:2013:CEC Learning the Caesar and Vigenere Cipher by Hierarchical Evolutionary Re-Combination
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Blair:2014:GECCOcomp Incremental evolution of HERCL programs for robust control
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Blair:2019:evomusart Adversarial Evolution and Deep Learning - How Does an Artist Play with Our Visual System?
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Blasco:2021:JSS An evolutionary approach for generating software models: The case of Kromaia in Game Software Engineering
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Blasco:2010:ARES Improving Network Intrusion Detection by Means of Domain-Aware Genetic Programming
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bleuler:2001:mgprbus Multiobjective Genetic Programming: Reducing Bloat Using SPEA2
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LotharThiele.html
EckartZitzler.html
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Bleuler:2008:MPSN Reducing Bloat in GP with Multiple Objectives
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EckartZitzler.html
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BT94 Genetic Programming and Redundancy
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blickle:1995:css A Comparison of Selection Schemes Used in Genetic Algorithms
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blickle:1995:ea Optimieren nach dem Vorbild der Natur, Evolutionare Algorithmen
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blickle:1995:YAGPLIC YAGPLIC User Manual
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blickle:1996:ecs Evolving Compact Solutions in Genetic Programming: A Case Study
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blickle96 Evolving Compact Solutions in Genetic Programming: A Case Study
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http___dx.doi.org_10.1007_3-540-61723-X_1020 http://dx.doi.org/10.1007/3-540-61723-X_1020
blickle:thesis Theory of Evolutionary Algorithms and Application to System Synthesis
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DBLP:journals/ec/BlickleT96 A Comparison of Selection Schemes used in Evolutionary Algorithms
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Blot:2019:GI Fuzzy Edit Sequences in Genetic Improvement
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Blot:2019:GI7 On Adaptive Specialisation in Genetic Improvement
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Blot:2020:EuroGP Comparing Genetic Programming Approaches for Non-Functional Genetic Improvement Case Study: Improvement of MiniSAT's Running Time
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Blot:2020:GIsp Stack-Based Genetic Improvement
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Blot:2020:GI Synthetic Benchmarks for Genetic Improvement
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blot:2021:tevc Empirical Comparison of Search Heuristics for Genetic Improvement of Software
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blot:hal-03595447 Using Genetic Improvement to Optimise Optimisation Algorithm Implementations
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blot:2022:corr_1 MAGPIE: Machine Automated General Performance Improvement via Evolution of Software
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Brezocnik:2002:JIM Genetic programming approach to determining of metal materials properties
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Brezocnik:2002:DAAAM On intelligent learning systems for next-generation manufacturing
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Brezocnik:2003:tmt Genetic-based approach to predict surface roughness in end milling
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Brownlee:2021:SEN Genetic Improvement @ ICSE 2021: Personal reflection of a Workshop Participant
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Brownlee:2023:SSBSE Enhancing Genetic Improvement Mutations Using Large Language Models
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brownlee:2024:GECCOcomp Genetic Improvement: Taking real-world source code and improving it using computational search methods
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Brownlee:2024:ASE Large Language Model Based Mutations in Genetic Improvement
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bruce2015reducing Reducing Energy Consumption Using Genetic Improvement
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Bruce:2015:gi Energy Optimisation via Genetic Improvement A SBSE technique for a new era in Software Development
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Bruce:2016:sigevolution A Report on the Genetic Improvement Workshop@GECCO 2016
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Bruce:2016:SSBSE Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV
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bruce:RN1701 Approximate Oracles and Synergy in Software Energy Search Spaces
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bruce:RN1707 Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV : A Correction
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Bruce:RN1804 Towards automatic generation and insertion of OpenACC directives
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bruce_bobby_r_thesis The Blind Software Engineer: Improving the Non-Functional Properties of Software by Means of Genetic Improvement
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Bruce:TSE Approximate Oracles and Synergy in Software Energy Search Spaces
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Bruce:2022:GI Automatically Exploring Computer System Design Spaces
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bruce:1996:agOOpGP Automatic Generation of Object-Oriented Programs Using Genetic Programming
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bruce:thesis The Application of Genetic Programming to the Automatic Generation of Object-Oriented Programs
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bruce:1997:lprsbGPADF The Lawnmower Problem Revisited: Stack-Based Genetic Programming and Automatically Defined Functions
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brucherseifer:2001:EuroGP An Indirect Block-Oriented Representation for Genetic Programming
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PeterBechtel.html
StephanFreyer.html
PeterMarenbach.html
http___dx.doi.org_10.1007_3-540-45355-5_21 http://dx.doi.org/10.1007/3-540-45355-5_21
Brucherseifer:thesis Der Artbegriff in der Genetischen Programmierung
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bruhn:2002:ECJ Genetic Programming over Context-Free Languages with Linear Constraints for the Knapsack Problem: First Results
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Brule:2016:ArXiv Evolving Shepherding Behavior with Genetic Programming Algorithms
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IsaacJulien.html
http___arxiv.org_abs_1603.06141 http://arxiv.org/abs/1603.06141
Brum:2018:evocop Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time
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MarcusRitt.html
http___dx.doi.org_10.1007_978-3-319-77449-7_6 http://dx.doi.org/10.1007/978-3-319-77449-7_6
Brum:thesis Automatic Algorithm Configuration for Flow Shop Scheduling Problems
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Brumby:1999:SPIE Investigation of image feature extraction by a genetic algorithm
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Brumby:2000:SPIE A genetic algorithm for combining new and existing image processing tools for multispectral imagery
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Brumby:2001:SPIE Evolving forest fire burn severity classification algorithms for multi-spectral imagery
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Brumby:2001:FUSION Genetic programming approach to extracting features from remotely sensed imagery
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NealRHarvey.html
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oai:CiteSeerPSU:445835 Evolving land cover classification algorithms for multispectral and multitemporal imagery
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Brun:2013:TR Evolution vs. Intelligent Design in Program Patching
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MingXiao.html
ClaireLeGoues.html
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brunello:2021:Sensors Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments
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BRUNELLO:2022:pmcj A genetic programming approach to WiFi fingerprint meta-distance learning
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NicolaSaccomanno.html
http___dx.doi.org_10.1016_j.pmcj.2022.101681 http://dx.doi.org/10.1016/j.pmcj.2022.101681
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Brunello:2023:ACC Monitors That Learn From Failures: Pairing STL and Genetic Programming
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DarioDellaMonica.html
AngeloMontanari.html
NicolaSaccomanno.html
AndreaUrgolo.html
http___dx.doi.org_10.1109_ACCESS.2023.3277620 http://dx.doi.org/10.1109/ACCESS.2023.3277620
BRUNS:2019:ESA Learning of complex event processing rules with genetic programming
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http___dx.doi.org_10.1016_j.eswa.2019.04.007 http://dx.doi.org/10.1016/j.eswa.2019.04.007
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10.1162/978-0-262-31050-5-ch003 Digital Evolution Exhibits Surprising Robustness to Poor Design Decisions
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BUCCHERI2021108722 Artificial intelligence in health data analysis: The Darwinian evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes
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DanieleDell'Aquila.html
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BUCCHERI2022100398 Stratified analysis of the age-related waist circumference cut-off model for the screening of dysglycemia at zero-cost
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DanieleDell'Aquila.html
MarcoRusso.html
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Buccheri:2024:JPT Appendicular Skeletal Muscle Mass in Older Adults Can Be Estimated With a Simple Equation Using a Few Zero-Cost Variables
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DanieleDell'Aquila.html
MarcoRusso.html
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buchanan:2024:CEC A Quality Diversity Study in EvoDevo Processes for Engineering Design
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RahulDubey.html
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MarkPrice.html
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BUCHNER:2020:IFAC-PapersOnLine An Artificial-Intelligence-Based Method to Automatically Create Interpretable Models from Data Targeting Embedded Control Applications
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SebastianBoblest.html
PatrickEngel.html
AndrejJunginger.html
HolgerUlmer.html
http___dx.doi.org_10.1016_j.ifacol.2020.12.887 http://dx.doi.org/10.1016/j.ifacol.2020.12.887
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eurogp06:BuchsbaumVossner Information-Dependent Switching of Identification Criteria in a Genetic Programming System for System Identification
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SiegfriedVossner.html
http___dx.doi.org_10.1007_11729976_27 http://dx.doi.org/10.1007/11729976_27
Buchsbaum:2007:cec Toward a Winning GP Strategy for Continuous Nonlinear Dynamical System Identification
ThomasBuchsbaum.html
http___dx.doi.org_10.1109_CEC.2007.4424616 http://dx.doi.org/10.1109/CEC.2007.4424616
Buchsbaum:thesis Improvement of Evolutionary Computation Approaches for Continuous Dynamical System Identification - Robustness and Performance Improvement of Standard Genetic Programming by Approximation, Multiple Shooting Methods, and Iterative Approaches
ThomasBuchsbaum.html
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Buckingham:2015:JH Inductive machine learning for improved estimation of catchment-scale snow water equivalent
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ChristianSkalka.html
JoshCBongard.html
http___dx.doi.org_10.1016_j.jhydrol.2015.02.042 http://dx.doi.org/10.1016/j.jhydrol.2015.02.042
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Buckley:2010:Chiong An Application of Genetic Programming to Forecasting Foreign Exchange Rates
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ZbigniewMichalewicz.html
RalfZurbruegg.html
http___hdl.handle.net_2440_54525 http://hdl.handle.net/2440/54525
http___dx.doi.org_10.4018_978-1-60566-705-8 http://dx.doi.org/10.4018/978-1-60566-705-8
Buckley:2009:niiiakd An Application of Genetic Programming to Forecasting Foreign Exchange Rates
MuneerBuckley.html
ZbigniewMichalewicz.html
RalfZurbruegg.html
http___www.igi-global.com_bookstore_chapter.aspx_titleid_36310 http://www.igi-global.com/bookstore/chapter.aspx?titleid=36310
http___hdl.handle.net_2440_54525 http://hdl.handle.net/2440/54525
Bucur:2014:ASC The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation
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GiovanniIacca.html
GiovanniSquillero.html
AlbertoTonda.html
http___www.sciencedirect.com_science_article_pii_S1568494613004213 http://www.sciencedirect.com/science/article/pii/S1568494613004213
http___dx.doi.org_10.1016_j.asoc.2013.12.002 http://dx.doi.org/10.1016/j.asoc.2013.12.002
Buffoni:2018:ICCC All-Implicants Neural Networks for Efficient Boolean Function Representation
FedericoBuffoni.html
GabrieleGianini.html
ErnestoDamiani.html
MichaelGranitzer.html
http___dx.doi.org_10.1109_ICCC.2018.00019 http://dx.doi.org/10.1109/ICCC.2018.00019
bui:286 Water Resource Engineers and Environmental Hydraulics
TaiDBui.html
AlanASmith.html
http___dx.doi.org_10.1061_40569_2001_286 http://dx.doi.org/10.1061/40569(2001)286
Bui:1997:s8p Solving the 8-Puzzle with Genetic Programming
ThaiBui.html
Buk:2009:ICANNGA NEAT in HyperNEAT Substituted with Genetic Programming
ZdenekBuk.html
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BogdanBurlacu.html
MichaelAffenzeller.html
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BogdanBurlacu.html
MichaelAffenzeller.html
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LukasKammerer.html
MichaelAffenzeller.html
GabrielKronberger.html
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BogdanBurlacu.html
MichaelAffenzeller.html
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BogdanBurlacu.html
MichaelAffenzeller.html
GabrielKronberger.html
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BogdanBurlacu.html
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MichaelKommenda.html
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BogdanBurlacu.html
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MichaelAffenzeller.html
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https___discovery.ucl.ac.uk_id_eprint_10189386_ https://discovery.ucl.ac.uk/id/eprint/10189386/
https___discovery.ucl.ac.uk_id_eprint_10189386_2_Improving_the_Non_Functional_Properties_of_Android_Applications_with_Genetic_Improvement_20_282_29.pdf https://discovery.ucl.ac.uk/id/eprint/10189386/2/Improving_the_Non_Functional_Properties_of_Android_Applications_with_Genetic_Improvement%20%282%29.pdf
Callan:2025:ASE Multi-objective improvement of Android applications
JamesCallan.html
JustynaPetke.html
https___rdcu.be_d0UCc https://rdcu.be/d0UCc
http___dx.doi.org_10.1007_s10515-024-00472-7 http://dx.doi.org/10.1007/s10515-024-00472-7
https___github.com_SOLAR-group_GIDroid https://github.com/SOLAR-group/GIDroid
Calumby:2014:ICIP Diversity-driven learning for multimodal image retrieval with relevance feedback
RodrigoTripodiCalumby.html
RicardodaSilvaTorres.html
MarcosAndreGoncalves.html
http___dx.doi.org_10.1109_ICIP.2014.7025445 http://dx.doi.org/10.1109/ICIP.2014.7025445
journals/mta/CalumbyTG14 Multimodal retrieval with relevance feedback based on genetic programming
RodrigoTripodiCalumby.html
RicardodaSilvaTorres.html
MarcosAndreGoncalves.html
http___dx.doi.org_10.1007_s11042-012-1152-7 http://dx.doi.org/10.1007/s11042-012-1152-7
Calvo-Fracasso:2018:CEC Multi-objective semantic mutation for genetic programming
JoaoVitorCalvoFracasso.html
FernandoJoseVonZuben.html
http___dx.doi.org_10.1109_CEC.2018.8477675 http://dx.doi.org/10.1109/CEC.2018.8477675
series/sci/Calzada-LedesmaSDOVS17 Comparing Grammatical Evolution's Mapping Processes on Feature Generation for Pattern Recognition Problems
ValentinCalzada-Ledesma.html
HectorJPuga.html
AlfonsoRojas-Dominguez.html
ManuelOrnelas-Rodriguez.html
JuanMartinCarpio-Valadez.html
ClaudiaGuadalupeGomezSantillan.html
http___dx.doi.org_10.1007_978-3-319-47054-2_52 http://dx.doi.org/10.1007/978-3-319-47054-2_52
Calzada-Ledesma:2018:IEEEAccess Evolutionary Design of Problem-Adapted Image Descriptors for Texture Classification
ValentinCalzada-Ledesma.html
HectorJPuga-Soberanes.html
ManuelOrnelas-Rodriguez.html
AlfonsoRojas-Dominguez.html
JuanMartinCarpio-Valadez.html
AndrisEspinal.html
JorgeAlbertoSoria-Alcaraz.html
MarcoAurelioSoteloFigueroa.html
http___dx.doi.org_10.1109_ACCESS.2018.2858660 http://dx.doi.org/10.1109/ACCESS.2018.2858660
Camargo-Bareno:2011:GECCOcomp Intrinsic evolvable hardware for combinatorial synthesis based on SoC+FPGA and GPU platforms
CarlosIvanCamargoBareno.html
CesarPedrazaBonilla.html
LuisFernandoNinoVasquez.html
JoseIgnacioMartinezTorre.html
http___dx.doi.org_10.1145_2001858.2001964 http://dx.doi.org/10.1145/2001858.2001964
Cambronero:thesis Mining Software Artifacts for use in Automated Machine Learning
JosePabloCambroneroSanchez.html
https___dspace.mit.edu_handle_1721.1_139465 https://dspace.mit.edu/handle/1721.1/139465
https___hdl.handle.net_1721.1_139465 https://hdl.handle.net/1721.1/139465
https___www.csail.mit.edu_event_mining-software-artifacts-use-automated-machine-learning https://www.csail.mit.edu/event/mining-software-artifacts-use-automated-machine-learning
https___www.josecambronero.com_pdf_JCambronero-PhD-EECS-June2021.pdf https://www.josecambronero.com/pdf/JCambronero-PhD-EECS-June2021.pdf
https___dspace.mit.edu_bitstream_handle_1721.1_139465_Cambronero-jcamsan-PhD-EECS-2021-thesis.pdf_sequence_1_isAllowed_y https://dspace.mit.edu/bitstream/handle/1721.1/139465/Cambronero-jcamsan-PhD-EECS-2021-thesis.pdf?sequence=1&isAllowed=y
campbell:2000:EGPDROR Evaluation of Genetic Programming for Determining Reservoir Operating Rules
ElliottCampbell.html
campbell:1993:MM Reviews
PaulJCampbell.html
http___links.jstor.org_sici_sici_0025-570X_28199304_2966_3A2_3C136_3AR_3E2.0.CO_3B2-4 http://links.jstor.org/sici?sici=0025-570X%28199304%2966%3A2%3C136%3AR%3E2.0.CO%3B2-4
CAMPOBELLO2020106488 Neuro-genetic programming for multigenre classification of music content
GiuseppeCampobello.html
DanieleDell'Aquila.html
MarcoRusso.html
AntoninoSegreto.html
http___www.sciencedirect.com_science_article_pii_S1568494620304270 http://www.sciencedirect.com/science/article/pii/S1568494620304270
http___dx.doi.org_10.1016_j.asoc.2020.106488 http://dx.doi.org/10.1016/j.asoc.2020.106488
Campos:GPEM Generating networks of genetic processors
MarcelinoCamposFrances.html
JoseMSempere.html
https___rdcu.be_czY20 https://rdcu.be/czY20
http___dx.doi.org_10.1007_s10710-021-09423-7 http://dx.doi.org/10.1007/s10710-021-09423-7
Can:2010:WSC Sequential metamodelling with genetic programming and particle swarms
BirkanCan.html
CathalHeavey.html
http___dx.doi.org_10.1109_WSC.2009.5429276 http://dx.doi.org/10.1109/WSC.2009.5429276
Can:thesis Evolutionary Modelling of Industrial Systems with Genetic Programming
BirkanCan.html
http___hdl.handle.net_10344_1693 http://hdl.handle.net/10344/1693
http___ulir.ul.ie_handle_10344_1693 http://ulir.ul.ie/handle/10344/1693
http___ulir.ul.ie_bitstream_handle_10344_1693_2010_Birkan_2c_20Can.pdf http://ulir.ul.ie/bitstream/handle/10344/1693/2010_Birkan%2c%20Can.pdf
Can2011 Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems
BirkanCan.html
CathalHeavey.html
http___dx.doi.org_10.1016_j.cie.2011.03.012 http://dx.doi.org/10.1016/j.cie.2011.03.012
Can2012424 A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models
BirkanCan.html
CathalHeavey.html
http___dx.doi.org_10.1016_j.cor.2011.05.004 http://dx.doi.org/10.1016/j.cor.2011.05.004
http___www.sciencedirect.com_science_article_pii_S0305054811001286 http://www.sciencedirect.com/science/article/pii/S0305054811001286
Can:2016:WSC A demonstration of machine learning for explicit functions for cycle time prediction using MES data
BirkanCan.html
CathalHeavey.html
http___dx.doi.org_10.1109_WSC.2016.7822289 http://dx.doi.org/10.1109/WSC.2016.7822289
journals/nca/CanakciBG09 Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming
HanifiCanakci.html
AdilBaykasoglu.html
HamzaGullu.html
http___dx.doi.org_10.1007_s00521-008-0208-0 http://dx.doi.org/10.1007/s00521-008-0208-0
CanhVu:2018:KSE Detect Wi-Fi Network Attacks Using Parallel Genetic Programming
VanCanhVu.html
Tuan-HaoHoang.html
http___dx.doi.org_10.1109_KSE.2018.8573378 http://dx.doi.org/10.1109/KSE.2018.8573378
Cani:2014:SACtr Towards Automated Malware Creation: Code Generation and Code Integration
AndreaCani.html
MarcoGaudesi.html
ErnestoSanchez.html
GiovanniSquillero.html
AlbertoTonda.html
http___www.cad.polito.it_downloads_White_papers_Towards_20Automated_20Malware_20Creation_20-_20Code_20Generation_20__20Code_20Integration.pdf http://www.cad.polito.it/downloads/White_papers/Towards%20Automated%20Malware%20Creation%20-%20Code%20Generation%20&%20Code%20Integration.pdf
Cani:2014:SACtr2 Towards Automated Malware Creation: Code Generation and Code Integration
AndreaCani.html
MarcoGaudesi.html
ErnestoSanchez.html
GiovanniSquillero.html
AlbertoTonda.html
http___www.cad.polito.it_2014_Cani_2014_SACtr2.pdf http://www.cad.polito.it/2014/Cani_2014_SACtr2.pdf
Cani:2014:SAC Towards automated malware creation: code generation and code integration
AndreaCani.html
MarcoGaudesi.html
ErnestoSanchez.html
GiovanniSquillero.html
AlbertoTonda.html
https___doi.org_10.1145_2554850.2555157 https://doi.org/10.1145/2554850.2555157
http___dx.doi.org_10.1145_2554850.2555157 http://dx.doi.org/10.1145/2554850.2555157
https___ugp3.sourceforge.net_ https://ugp3.sourceforge.net/
Cankorur-Cetinkaya:2017:MB CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology
AycaCankorur-Cetinkaya.html
JoaoMLDias.html
JanaKludas.html
NigelKHSlater.html
JuhoRousu.html
StephenGOliver.html
DuyguDikicioglu.html
http___dx.doi.org_10.1099_mic.0.000477 http://dx.doi.org/10.1099/mic.0.000477
Cano:2010:HAIS Solving Classification Problems Using Genetic Programming Algorithms on GPUs
AlbertoCanoRojas.html
AmeliaZafraGomez.html
SebastianVentura.html
http___dx.doi.org_10.1007_978-3-642-13803-4_3 http://dx.doi.org/10.1007/978-3-642-13803-4_3
Cano:2011:SC Speeding up the evaluation phase of GP classification algorithms on GPUs
AlbertoCanoRojas.html
AmeliaZafraGomez.html
SebastianVentura.html
http___dx.doi.org_10.1007_s00500-011-0713-4 http://dx.doi.org/10.1007/s00500-011-0713-4
conf/hais/CanoZV11 A Parallel Genetic Programming Algorithm for Classification
AlbertoCanoRojas.html
AmeliaZafraGomez.html
SebastianVentura.html
http___dx.doi.org_10.1007_978-3-642-21219-2_23 http://dx.doi.org/10.1007/978-3-642-21219-2_23
cano:2013:EuroGP A Grammar-Guided Genetic Programming Algorithm for Multi-Label Classification
AlbertoCanoRojas.html
AmeliaZafraGomez.html
EvaLGibaja.html
SebastianVentura.html
http___dx.doi.org_10.1007_978-3-642-37207-0_19 http://dx.doi.org/10.1007/978-3-642-37207-0_19
Cano:2013:JPDC Parallel multi-objective Ant Programming for classification using GPUs
AlbertoCanoRojas.html
JuanLuisOlmo.html
SebastianVentura.html
http___dx.doi.org_10.1016_j.jpdc.2013.01.017 http://dx.doi.org/10.1016/j.jpdc.2013.01.017
Cano:2013:INS An Interpretable Classification Rule Mining Algorithm
AlbertoCanoRojas.html
AmeliaZafraGomez.html
SebastianVentura.html
http___www.sciencedirect.com_science_article_pii_S0020025513002430 http://www.sciencedirect.com/science/article/pii/S0020025513002430
http___dx.doi.org_10.1016_j.ins.2013.03.038 http://dx.doi.org/10.1016/j.ins.2013.03.038
Cano:2013:JSUP High performance evaluation of evolutionary-mined association rules on GPUs
AlbertoCanoRojas.html
JoseMariaLuna.html
SebastianVentura.html
http___link.springer.com_article_10.1007_s11227-013-0937-4_fulltext.html http://link.springer.com/article/10.1007/s11227-013-0937-4/fulltext.html
http___dx.doi.org_10.1007_s11227-013-0937-4 http://dx.doi.org/10.1007/s11227-013-0937-4
Thesis_Alberto_Cano New Classification Models through Evolutionary Algorithms
AlbertoCanoRojas.html
https___www.people.vcu.edu__acano_pdf_Thesis_20Alberto_20Cano.pdf https://www.people.vcu.edu/~acano/pdf/Thesis%20Alberto%20Cano.pdf
https___www.uco.es_kdis_research_theses_thesis-acano_ https://www.uco.es/kdis/research/theses/thesis-acano/
http___www.uco.es_grupos_kdis_docs_thesis_2014-ACano.pdf http://www.uco.es/grupos/kdis/docs/thesis/2014-ACano.pdf
Cano:2014:GECCO GPU-parallel subtree interpreter for genetic programming
AlbertoCanoRojas.html
SebastianVentura.html
http___doi.acm.org_10.1145_2576768.2598272 http://doi.acm.org/10.1145/2576768.2598272
http___dx.doi.org_10.1145_2576768.2598272 http://dx.doi.org/10.1145/2576768.2598272
2014-KAIS-Cano Speeding up multiple instance learning classification rules on GPUs
AlbertoCanoRojas.html
AmeliaZafraGomez.html
SebastianVentura.html
http___dx.doi.org_10.1007_s10115-014-0752-0 http://dx.doi.org/10.1007/s10115-014-0752-0
Cano:2015:JMLR A Classification Module for Genetic Programming Algorithms in JCLEC
AlbertoCanoRojas.html
JoseMariaLuna.html
AmeliaZafraGomez.html
SebastianVentura.html
http___www.jmlr.org_ http://www.jmlr.org/
http___www.jmlr.org_papers_v16_cano15a.html http://www.jmlr.org/papers/v16/cano15a.html
http___www.jmlr.org_papers_volume16_cano15a_cano15a.pdf http://www.jmlr.org/papers/volume16/cano15a/cano15a.pdf
Cano:2016:SC Multi-objective genetic programming for feature extraction and data visualization
AlbertoCanoRojas.html
SebastianVentura.html
KrzysztofJCios.html
http___dx.doi.org_10.1007_s00500-015-1907-y http://dx.doi.org/10.1007/s00500-015-1907-y
CANO:2019:PR Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams
AlbertoCanoRojas.html
BartoszKrawczyk.html
http___dx.doi.org_10.1016_j.patcog.2018.10.024 http://dx.doi.org/10.1016/j.patcog.2018.10.024
http___www.sciencedirect.com_science_article_pii_S0031320318303765 http://www.sciencedirect.com/science/article/pii/S0031320318303765
Cano:2019:LT Interpretable Multiview Early Warning System Adapted to Underrepresented Student Populations
AlbertoCanoRojas.html
JohnDLeonardII.html
http___dx.doi.org_10.1109_TLT.2019.2911079 http://dx.doi.org/10.1109/TLT.2019.2911079
cano:2023:GECCOcomp Hardware Design of a Model Generator Based on Grammars and Cartesian Genetic Programming for Blood Glucose Prediction
JorgeCano.html
JoseIgnacioHidalgoPerez.html
OscarGarnica.html
JLanchares.html
http___dx.doi.org_10.1145_3583133.3596427 http://dx.doi.org/10.1145/3583133.3596427
Cano:2025:GPEM Hardware real-time individualised blood glucose predictor generator based on grammars and cartesian genetic programming
JorgeCano.html
JoseIgnacioHidalgoPerez.html
OscarGarnica.html
http___dx.doi.org_10.1007_s10710-024-09500-7 http://dx.doi.org/10.1007/s10710-024-09500-7
cantner:2001:JASSS Empirically Based Simulation: The Case of Twin Peaks in National Income
UweVCantner.html
BerndEbersberger.html
HorstHanusch.html
JensJKruger.html
AndreasPyka.html
http___jasss.soc.surrey.ac.uk_4_3_9.html http://jasss.soc.surrey.ac.uk/4/3/9.html
cantu-paz:2002:gecco:lbp Late Breaking papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
ErickCantu-Paz.html
http___gpbib.cs.ucl.ac.uk_gecco2002lb.bib http://gpbib.cs.ucl.ac.uk/gecco2002lb.bib
GECCO2003-PartI Genetic and Evolutionary Computation -- GECCO 2003, Part I
ErickCantu-Paz.html
JamesAFoster.html
KalyanmoyDeb.html
LawrenceDavis.html
RajkumarRoy.html
Una-MayO'Reilly.html
Hans-GeorgBeyer.html
RussellKStandish.html
GrahamKendall.html
StewartWWilson.html
MarkHarman.html
JoachimWegener.html
DipankarDasgupta.html
MitchellAPotter.html
AlanCSchultz.html
KathrynADowsland.html
NatashaJonoska.html
JulianFMiller.html
http___dx.doi.org_10.1007_3-540-45105-6 http://dx.doi.org/10.1007/3-540-45105-6
GECCO2003-PartII Genetic and Evolutionary Computation -- GECCO 2003, Part II
ErickCantu-Paz.html
JamesAFoster.html
KalyanmoyDeb.html
LawrenceDavis.html
RajkumarRoy.html
Una-MayO'Reilly.html
Hans-GeorgBeyer.html
RussellKStandish.html
GrahamKendall.html
StewartWWilson.html
MarkHarman.html
JoachimWegener.html
DipankarDasgupta.html
MitchellAPotter.html
AlanCSchultz.html
KathrynADowsland.html
NatashaJonoska.html
JulianFMiller.html
http___dx.doi.org_10.1007_3-540-45110-2 http://dx.doi.org/10.1007/3-540-45110-2
Cao:2016:ICISCE Increasing Diversity and Controlling Bloat in Linear Genetic Programming
BoCao.html
ZongliJiang.html
http___dx.doi.org_10.1109_ICISCE.2016.97 http://dx.doi.org/10.1109/ICISCE.2016.97
Cao:2020:ISSSR A Survey on Automatic Bug Fixing
HelingCao.html
YangxiaMeng.html
JianshuShi.html
LeiLi.html
TiaoliLiao.html
ChenyangZhao.html
http___dx.doi.org_10.1109_ISSSR51244.2020.00029 http://dx.doi.org/10.1109/ISSSR51244.2020.00029
Cao:2021:QRS-C Automated Repair of Java Programs with Random Search via Code Similarity
HelingCao.html
FangzhengLiu.html
JianshuShi.html
YongheChu.html
MiaoleiDeng.html
http___dx.doi.org_10.1109_QRS-C55045.2021.00075 http://dx.doi.org/10.1109/QRS-C55045.2021.00075
cao:1998:NPSC A Hybrid Evolutionary Modeling Algorithm for System of Ordinary Differential Equations
Hong-QingCao.html
Li-ShanKang.html
ZbigniewMichalewicz.html
Yu-PingChen.html
http___www.dynamicpublishers.com_Neural_neuralv6.htm http://www.dynamicpublishers.com/Neural/neuralv6.htm
http___dl.acm.org_citation.cfm_id_293731.293733 http://dl.acm.org/citation.cfm?id=293731.293733
cao:1998:2eaode A Two-level Evolutionary Algorithm for Modeling System of Ordinary Differential Equations
Hong-QingCao.html
Li-ShanKang.html
ZbigniewMichalewicz.html
Yu-PingChen.html
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cao:1999:CC The Kinetic Evolutionary Modeling of Complex Systems of Chemical Reactions
Hong-QingCao.html
JingxianYu.html
Li-ShanKang.html
Yu-PingChen.html
YongyanChen.html
http___dx.doi.org_10.1016_S0097-8485_99_00005-4 http://dx.doi.org/10.1016/S0097-8485(99)00005-4
cao:1999:EMODEDS Evolutionary Modeling of Ordinary Differential Equations for Dynamic Systems
Hong-QingCao.html
Li-ShanKang.html
Yu-PingChen.html
http___gpbib.cs.ucl.ac.uk_gecco1999_GP-401.pdf http://gpbib.cs.ucl.ac.uk/gecco1999/GP-401.pdf
http___gpbib.cs.ucl.ac.uk_gecco1999_GP-401.ps http://gpbib.cs.ucl.ac.uk/gecco1999/GP-401.ps
cao:2000:odeGP Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming
Hong-QingCao.html
Li-ShanKang.html
Yu-PingChen.html
JingxianYu.html
http___www.ees.adelaide.edu.au_people_enviro_cao_2000-05.pdf http://www.ees.adelaide.edu.au/people/enviro/cao/2000-05.pdf
http___dx.doi.org_10.1023_A_1010013106294 http://dx.doi.org/10.1023/A:1010013106294
cao:2000:ode2GP A two-level hybrid evolutionary algorithm for modeling one-dimensional dynamic systems by higher-order ODE models
Hong-QingCao.html
Li-ShanKang.html
TaoGuo.html
Yu-PingChen.html
HugodeGaris.html
http___ieeexplore.ieee.org_iel5_3477_18067_00836383.pdf http://ieeexplore.ieee.org/iel5/3477/18067/00836383.pdf
cao:2001:CC Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms
Hong-QingCao.html
JingxianYu.html
Li-ShanKang.html
HanxiYang.html
XinpingAi.html
http___dx.doi.org_10.1016_S0097-8485_00_00099-1 http://dx.doi.org/10.1016/S0097-8485(00)00099-1
cao:2003:WUJNS Parallel Implementations of Modeling Dynamical Systems by Using System of Ordinary Differential Equations
Hong-QingCao.html
Li-ShanKang.html
JingxianYu.html
http___dx.doi.org_10.1007_BF02899484 http://dx.doi.org/10.1007/BF02899484
cao:2003:NPSC An Experimental Study of Some Control Parameters in Parallel Genetic Programming
Hong-QingCao.html
JingxianYu.html
Li-ShanKang.html
RI_Bob_McKay.html
http___citeseerx.ist.psu.edu_viewdoc_download_doi_10.1.1.80.6377.pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.6377.pdf
cao:2003:CMA The Dynamic Evolutionary Modeling of HODEs for Time Series Prediction
Hong-QingCao.html
Li-ShanKang.html
Yu-PingChen.html
TaoGuo.html
http___www.sciencedirect.com_science_article_B6TYJ-4BRR761-P_2_4d226ed6e682798de2e1d83d01cebd95 http://www.sciencedirect.com/science/article/B6TYJ-4BRR761-P/2/4d226ed6e682798de2e1d83d01cebd95
http___dx.doi.org_10.1016_S0898-1221_03_90228-8 http://dx.doi.org/10.1016/S0898-1221(03)90228-8
Cao:2003:Aeafmtecfeis An evolutionary approach for modeling the equivalent circuit for electrochemical impedance spectroscopy
Hong-QingCao.html
JingxianYu.html
Li-ShanKang.html
http___www.ees.adelaide.edu.au_people_enviro_cao_2003-05.pdf http://www.ees.adelaide.edu.au/people/enviro/cao/2003-05.pdf
http___dx.doi.org_10.1109_CEC.2003.1299893 http://dx.doi.org/10.1109/CEC.2003.1299893
Cao:2006:EI Discovery of Predictive Rule Sets for Chlorophyll-a Dynamics in the Nakdong River (Korea) by Means of the Hybrid Evolutionary Algorithm HEA
Hong-QingCao.html
FriedrichRecknagel.html
Gea-JaeJoo.html
Dong-KyunKim.html
http___dx.doi.org_10.1016_j.ecoinf.2005.08.001 http://dx.doi.org/10.1016/j.ecoinf.2005.08.001
Cao:2006:2lakes Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication
Hong-QingCao.html
FriedrichRecknagel.html
BomchulKim.html
NorikoTakamura.html
http___dx.doi.org_10.1007_3-540-28426-5_17 http://dx.doi.org/10.1007/3-540-28426-5_17
Cao2008181 Process-based simulation library SALMO-OO for lake ecosystems. Part 2: Multi-objective parameter optimization by evolutionary algorithms
Hong-QingCao.html
FriedrichRecknagel.html
LydiaCetin.html
ByronHeZhang.html
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DBLP:journals/corr/abs-2103-12623 Evolving Learning Rate Optimizers for Deep Neural Networks
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Carvalho:2022:EuroGP Evolving Adaptive Neural Network Optimizers for Image Classification
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http___dx.doi.org_10.1007_978-3-031-02056-8_1 http://dx.doi.org/10.1007/978-3-031-02056-8_1
Carvalho:2023:EuroGP Context Matters: Adaptive Mutation for Grammars
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Carvalho:2021:IAM Using Grammatical Evolution for Modelling Energy Consumption on a Computer Numerical Control Machine
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Casadei:2019:BRACIS A Multi-objective Approach for Symbolic Regression with Semantic Genetic Programming
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Casanova:2010:cec Tradinnova-LCS: Dynamic stock portfolio decision-making assistance model with genetic based machine learning
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Edgar_Enrique_Casasola_Murillo Desarrollo de un modelo computacional para la especificacion de sistemas de analisis de sentimiento con comentarios de redes sociales en espanol
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DBLP:conf/kbse/CashinMWF19 Understanding Automatically-Generated Patches Through Symbolic Invariant Differences
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Casjens_Dissertation Adaption und Vergleich evolutionaerer mehrkriterieller Algorithmen mit Hilfe von Variablenwichtigkeitsmassen
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https___eldorado.tu-dortmund.de_bitstream_2003_30431_1_Casjens_Dissertation.pdf https://eldorado.tu-dortmund.de/bitstream/2003/30431/1/Casjens_Dissertation.pdf
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http___dx.doi.org_10.17877_DE290R-5588 http://dx.doi.org/10.17877/DE290R-5588
CASTEJON20181003 Automatic design of analog electronic circuits using grammatical evolution
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http___dx.doi.org_10.1016_j.asoc.2017.09.036 http://dx.doi.org/10.1016/j.asoc.2017.09.036
Castejon:2020:ACC Introducing Modularity and Homology in Grammatical Evolution to Address the Analog Electronic Circuit Design Problem
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http___dx.doi.org_10.1109_ACCESS.2020.3011641 http://dx.doi.org/10.1109/ACCESS.2020.3011641
Castellano:2021:SBST Frenetic at the SBST 2021 Tool Competition
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castellanos-garzon:2019:PACBBIC A Genetic Programming Approach Applied to Feature Selection from Medical Data
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http___dx.doi.org_10.1007_978-3-319-98702-6_24 http://dx.doi.org/10.1007/978-3-319-98702-6_24
castellanos-garzon:2020:Processes A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis
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JoseLuisJaimesSanchez.html
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https___www.mdpi.com_2227-9717_8_12_1565 https://www.mdpi.com/2227-9717/8/12/1565
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CASTELLANOSGARZON:2019:KS An evolutionary framework for machine learning applied to medical data
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ErnestoCosta.html
JoseLuisJaimesS.html
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http___dx.doi.org_10.1016_j.knosys.2019.104982 http://dx.doi.org/10.1016/j.knosys.2019.104982
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CASTELLANOSGARZON:2020:MethodsX Determining the maximum length of logical rules in a classifier and visual comparison of results
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ErnestoCosta.html
JoseLuisSJaimes.html
JuanMCorchado.html
http___dx.doi.org_10.1016_j.mex.2020.100846 http://dx.doi.org/10.1016/j.mex.2020.100846
http___www.sciencedirect.com_science_article_pii_S2215016120300650 http://www.sciencedirect.com/science/article/pii/S2215016120300650
Castelli:2010:cec A comparison of the generalization ability of different genetic programming frameworks
MauroCastelli.html
LucaManzoni.html
SaraSilva.html
LeonardoVanneschi.html
http___dx.doi.org_10.1109_CEC.2010.5585925 http://dx.doi.org/10.1109/CEC.2010.5585925
castelli:2011:EuroGP A Quantitative Study of Learning and Generalization in Genetic Programming
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LucaManzoni.html
SaraSilva.html
LeonardoVanneschi.html
http___dx.doi.org_10.1007_978-3-642-20407-4_3 http://dx.doi.org/10.1007/978-3-642-20407-4_3
conf/lion/CastelliMV11 Multi Objective Genetic Programming for Feature Construction in Classification Problems
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LucaManzoni.html
LeonardoVanneschi.html
http___dx.doi.org_10.1007_978-3-642-25566-3_39 http://dx.doi.org/10.1007/978-3-642-25566-3_39
Castelli:thesis Measures and Methods for Robust Genetic Programming
MauroCastelli.html
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Castelli:2012:GECCO Parameter tuning of evolutionary reactions systems
MauroCastelli.html
LucaManzoni.html
LeonardoVanneschi.html
http___dx.doi.org_10.1145_2330163.2330265 http://dx.doi.org/10.1145/2330163.2330265
Castelli:2012:arXiv An Efficient Genetic Programming System with Geometric Semantic Operators and its Application to Human Oral Bioavailability Prediction
MauroCastelli.html
LucaManzoni.html
LeonardoVanneschi.html
http___arxiv.org_abs_1208.2437 http://arxiv.org/abs/1208.2437
Castelli:evoapps13 Land Cover/Land Use Multiclass Classification Using GP with Geometric Semantic Operators
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SaraSilva.html
LeonardoVanneschi.html
AnaIsabelRosaCabral.html
MariaJoseVasconcelos.html
LuisCatarino.html
JoaoManueldeBritoCarreiras.html
http___dx.doi.org_10.1007_978-3-642-37192-9_34 http://dx.doi.org/10.1007/978-3-642-37192-9_34
Castelli:2013:ieeeCybernetics Semantic Search-Based Genetic Programming and the Effect of Intron Deletion
MauroCastelli.html
LeonardoVanneschi.html
SaraSilva.html
http___dx.doi.org_10.1109_TSMCC.2013.2247754 http://dx.doi.org/10.1109/TSMCC.2013.2247754
Castelli:2013:GECCOcomp An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics
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DavideCastaldi.html
LeonardoVanneschi.html
IlariaGiordani.html
FrancescoArchetti.html
DanieleMaccagnola.html
http___dx.doi.org_10.1145_2464576.2464644 http://dx.doi.org/10.1145/2464576.2464644
Castelli:2013:EPIA An Efficient Implementation of Geometric Semantic Genetic Programming for Anticoagulation Level Prediction in Pharmacogenetics
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DavideCastaldi.html
IlariaGiordani.html
SaraSilva.html
LeonardoVanneschi.html
FrancescoArchetti.html
DanieleMaccagnola.html
http___link.springer.com_chapter_10.1007_978-3-642-40669-0_8 http://link.springer.com/chapter/10.1007/978-3-642-40669-0_8
http___dx.doi.org_10.1007_978-3-642-40669-0_8 http://dx.doi.org/10.1007/978-3-642-40669-0_8
Castelli:2013:ESA Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators
MauroCastelli.html
LeonardoVanneschi.html
SaraSilva.html
http___dx.doi.org_10.1016_j.eswa.2013.06.037 http://dx.doi.org/10.1016/j.eswa.2013.06.037
http___www.sciencedirect.com_science_article_pii_S0957417413004326 http://www.sciencedirect.com/science/article/pii/S0957417413004326
Castelli:2014:ieeeCybernetics Semantic Search-Based Genetic Programming and the Effect of Intron Deletion
MauroCastelli.html
LeonardoVanneschi.html
SaraSilva.html
http___dx.doi.org_10.1109_TSMCC.2013.2247754 http://dx.doi.org/10.1109/TSMCC.2013.2247754
Castelli:2014:ESA Prediction of the Unified Parkinson's Disease Rating Scale assessment using a genetic programming system with geometric semantic genetic operators
MauroCastelli.html
LeonardoVanneschi.html
SaraSilva.html
http___dx.doi.org_10.1016_j.eswa.2014.01.018 http://dx.doi.org/10.1016/j.eswa.2014.01.018
http___www.sciencedirect.com_science_article_pii_S0957417414000396 http://www.sciencedirect.com/science/article/pii/S0957417414000396
Castelli:2014:SMGP The Influence of Population Size on Geometric Semantic GP
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LucaManzoni.html
SaraSilva.html
LeonardoVanneschi.html
http___www.cs.put.poznan.pl_kkrawiec_smgp2014_uploads_Site_Castelli.pdf http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Castelli.pdf
Castelli:2014:SMGP2 Self-tuning Geometric Semantic GP
MauroCastelli.html
LucaManzoni.html
SaraSilva.html
LeonardoVanneschi.html
http___www.cs.put.poznan.pl_kkrawiec_smgp2014_uploads_Site_Castelli2.pdf http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Castelli2.pdf
Castelli:2014:GPEM A C++ framework for geometric semantic genetic programming
MauroCastelli.html
SaraSilva.html
LeonardoVanneschi.html
http___dx.doi.org_10.1007_s10710-014-9218-0 http://dx.doi.org/10.1007/s10710-014-9218-0
Castelli:2014:Cybernetics Corrections to ``Semantic Search Based Genetic Programming and the Effect of Introns Deletion'' [Jan 14 103-113]
MauroCastelli.html
LeonardoVanneschi.html
SaraSilva.html
http___dx.doi.org_10.1109_TCYB.2014.2303551 http://dx.doi.org/10.1109/TCYB.2014.2303551
Castelli:2014:GPTP How to Exploit Alignment in the Error Space: Two Different GP Models
MauroCastelli.html
LeonardoVanneschi.html
SaraSilva.html
StefanoRuberto.html
http___dx.doi.org_10.1007_978-3-319-16030-6_8 http://dx.doi.org/10.1007/978-3-319-16030-6_8
Castelli:2015:Neurocomputing A geometric semantic genetic programming system for the electoral redistricting problem
MauroCastelli.html
RobertoHenriques.html
LeonardoVanneschi.html
http___dx.doi.org_10.1016_j.neucom.2014.12.003 http://dx.doi.org/10.1016/j.neucom.2014.12.003
http___www.sciencedirect.com_science_article_pii_S0925231214016671 http://www.sciencedirect.com/science/article/pii/S0925231214016671
Castelli:2015:EE Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case
MauroCastelli.html
LeonardoVanneschi.html
MatteoDeFelice.html
http___dx.doi.org_10.1016_j.eneco.2014.10.009 http://dx.doi.org/10.1016/j.eneco.2014.10.009
http___www.sciencedirect.com_science_article_pii_S0140988314002539 http://www.sciencedirect.com/science/article/pii/S0140988314002539
Castelli:2015:FireEcology Predicting Burned Areas of Forest Fires: an Artificial Intelligence Approach
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LeonardoVanneschi.html
AlesPopovic.html
http___dx.doi.org_10.4996_fireecology.1101106 http://dx.doi.org/10.4996/fireecology.1101106
Castelli:2015:CINS Energy Consumption Forecasting using Semantics Based Genetic Programming with Local Search Optimizer
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LeonardoVanneschi.html
LeonardoTrujillo.html
http___www.ncbi.nlm.nih.gov_pmc_articles_PMC4464001_ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464001/
http___www.ncbi.nlm.nih.gov_pubmed_26106410 http://www.ncbi.nlm.nih.gov/pubmed/26106410
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http___dx.doi.org_10.1155_2015_971908 http://dx.doi.org/10.1155/2015/971908
Castelli:2015:GECCO Geometric Semantic Genetic Programming with Local Search
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LeonardoVanneschi.html
SaraSilva.html
EmigdioZ-Flores.html
PierrickLegrand.html
http___doi.acm.org_10.1145_2739480.2754795 http://doi.acm.org/10.1145/2739480.2754795
http___dx.doi.org_10.1145_2739480.2754795 http://dx.doi.org/10.1145/2739480.2754795
conf/epia/CastelliFMV15 Electricity Demand Modelling with Genetic Programming
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LeonardoVanneschi.html
http___dx.doi.org_10.1007_978-3-319-23485-4 http://dx.doi.org/10.1007/978-3-319-23485-4
Castelli:2015:EB Prediction of energy performance of residential buildings: a genetic programming approach
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AlesPopovic.html
http___www.sciencedirect.com_science_article_pii_S0378778815003849 http://www.sciencedirect.com/science/article/pii/S0378778815003849
http___dx.doi.org_10.1016_j.enbuild.2015.05.013 http://dx.doi.org/10.1016/j.enbuild.2015.05.013
Castelli:2016:GPEM Self-tuning geometric semantic Genetic Programming
MauroCastelli.html
LucaManzoni.html
LeonardoVanneschi.html
SaraSilva.html
AlesPopovic.html
http___dx.doi.org_10.1007_s10710-015-9251-7 http://dx.doi.org/10.1007/s10710-015-9251-7
Castelli:2015:ASC Prediction of relative position of CT slices using a computational intelligence system
MauroCastelli.html
LeonardoTrujillo.html
LeonardoVanneschi.html
AlesPopovic.html
http___dx.doi.org_10.1016_j.asoc.2015.09.021 http://dx.doi.org/10.1016/j.asoc.2015.09.021
http___www.sciencedirect.com_science_article_pii_S1568494615005931 http://www.sciencedirect.com/science/article/pii/S1568494615005931
Castelli:2016:SEC Semantic genetic programming for fast and accurate data knowledge discovery
MauroCastelli.html
LeonardoVanneschi.html
LucaManzoni.html
AlesPopovic.html
http___dx.doi.org_10.1016_j.swevo.2015.07.001 http://dx.doi.org/10.1016/j.swevo.2015.07.001
http___www.sciencedirect.com_science_article_pii_S2210650215000516 http://www.sciencedirect.com/science/article/pii/S2210650215000516
Castelli:2016:IJBIC Parameter evaluation of geometric semantic genetic programming in pharmacokinetics
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LeonardoVanneschi.html
AlesPopovic.html
http___www.inderscience.com_link.php_id_74634 http://www.inderscience.com/link.php?id=74634
http___dx.doi.org_10.1504_IJBIC.2016.074634 http://dx.doi.org/10.1504/IJBIC.2016.074634
Castelli:2016:CIN Controlling Individuals Growth in Semantic Genetic Programming through Elitist Replacement
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AlesPopovic.html
http___dx.doi.org_10.1155_2016_8326760 http://dx.doi.org/10.1155/2016/8326760
http___downloads.hindawi.com_journals_cin_2016_8326760.pdf http://downloads.hindawi.com/journals/cin/2016/8326760.pdf
castelli2016analysis An Analysis of Geometric Semantic Crossover: A Computational Geometry Approach
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LucaManzoni.html
IvoGoncalves.html
LeonardoVanneschi.html
LeonardoTrujillo.html
SaraSilva.html
http___dx.doi.org_10.5220_0006056402010208 http://dx.doi.org/10.5220/0006056402010208
castelli:2017:jaihc Predicting per capita violent crimes in urban areas: an artificial intelligence approach
MauroCastelli.html
RaulSormani.html
LeonardoTrujillo.html
AlesPopovic.html
http___dx.doi.org_10.1007_s12652-015-0334-3 http://dx.doi.org/10.1007/s12652-015-0334-3
journals/ijbic/CastelliVTP17 Stock index return forecasting: semantics-based genetic programming with local search optimiser
MauroCastelli.html
LeonardoVanneschi.html
LeonardoTrujillo.html
AlesPopovic.html
http___dx.doi.org_10.1504_IJBIC.2017.10004325 http://dx.doi.org/10.1504/IJBIC.2017.10004325
Castelli:2017:GP EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming
MauroCastelli.html
JamesMcDermott.html
LukasSekanina.html
http___dx.doi.org_10.1007_978-3-319-55696-3 http://dx.doi.org/10.1007/978-3-319-55696-3
Castelli:2017:SEC The influence of population size in geometric semantic GP
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SaraSilva.html
LeonardoVanneschi.html
AlesPopovic.html
http___dx.doi.org_10.1016_j.swevo.2016.05.004 http://dx.doi.org/10.1016/j.swevo.2016.05.004
http___www.sciencedirect.com_science_article_pii_S2210650216300256 http://www.sciencedirect.com/science/article/pii/S2210650216300256
Castelli:2017:ESA An expert system for extracting knowledge from customers' reviews: The case of Amazon.com, Inc.
MauroCastelli.html
LucaManzoni.html
LeonardoVanneschi.html
AlesPopovic.html
http___dx.doi.org_10.1016_j.eswa.2017.05.008 http://dx.doi.org/10.1016/j.eswa.2017.05.008
http___www.sciencedirect.com_science_article_pii_S0957417417303263 http://www.sciencedirect.com/science/article/pii/S0957417417303263
Castelli:2017:CandC An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming
MauroCastelli.html
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IvoGoncalves.html
AlesPopovic.html
http___www.techno-press.org__page_container_journal_cac_volume_19_num_6 http://www.techno-press.org/?page=container&journal=cac&volume=19&num=6
http___dx.doi.org_10.12989_cac.2017.19.6.651 http://dx.doi.org/10.12989/cac.2017.19.6.651
castelli2017evolutionary An evolutionary system for ozone concentration forecasting
MauroCastelli.html
IvoGoncalves.html
LeonardoTrujillo.html
AlesPopovic.html
http___dx.doi.org_10.1007_s10796-016-9706-2 http://dx.doi.org/10.1007/s10796-016-9706-2
Castelli:2018:GP EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming
MauroCastelli.html
LukasSekanina.html
MengjieZhang.html
https___link.springer.com_book_10.1007_2F978-3-319-77553-1 https://link.springer.com/book/10.1007%2F978-3-319-77553-1
http___dx.doi.org_10.1007_978-3-319-77553-1 http://dx.doi.org/10.1007/978-3-319-77553-1
Castelli:2018:EuroGP Pruning Techniques for Mixed Ensembles of Genetic Programming Models
MauroCastelli.html
IvoGoncalves.html
LucaManzoni.html
LeonardoVanneschi.html
http___dx.doi.org_10.1007_978-3-319-77553-1_4 http://dx.doi.org/10.1007/978-3-319-77553-1_4
DBLP:conf/epia/CastelliMMS19 Extending Local Search in Geometric Semantic Genetic Programming
MauroCastelli.html
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AkashPunhani.html
http___dx.doi.org_doi_10.4018_IJAMC.2019010107 http://dx.doi.org/doi=10.4018/IJAMC.2019010107
Chia-Lan.Chang:masters Dynamic Proportion Portfolio Insurance with Genetic Programming and Market Volatility Factors Analysis
Chia-LanChang.html
http___ir.lib.ncu.edu.tw_handle_987654321_13148 http://ir.lib.ncu.edu.tw/handle/987654321/13148
chang:2023:GECCOcomp Taylor Polynomial Enhancer Using Genetic Programming for Symbolic Regression
Chi-HsienChang.html
Tu-ChinChiang.html
Tzu-HaoHsu.html
Ting-ShuoChuang.html
Wen-ZhongFang.html
Tian-LiYu.html
http___dx.doi.org_10.1145_3583133.3590591 http://dx.doi.org/10.1145/3583133.3590591
Chang:2012:IIH-MSP A Genetic Programming Based Scheme for Combining Image Operators
Feng-ChengChang.html
Hsiang-ChehHuang.html
http___dx.doi.org_10.1109_IIH-MSP.2012.58 http://dx.doi.org/10.1109/IIH-MSP.2012.58
Chang:2013:IIH-MSP Experiments on Genetic Programming Based Image Artefact Detection
Feng-ChengChang.html
Hsiang-ChehHuang.html
http___dx.doi.org_10.1109_IIH-MSP.2013.11 http://dx.doi.org/10.1109/IIH-MSP.2013.11
chang:2017:AIIHMSP A Design of Genetic Programming Scheme with VLIW Concepts
Feng-ChengChang.html
Hsiang-ChehHuang.html
http___link.springer.com_chapter_10.1007_978-3-319-50212-0_37 http://link.springer.com/chapter/10.1007/978-3-319-50212-0_37
http___dx.doi.org_10.1007_978-3-319-50212-0_37 http://dx.doi.org/10.1007/978-3-319-50212-0_37
Chang:2022:LifeTech Conditionals Support in Binary Expression Tree Based Genetic Programming
Feng-ChengChang.html
Hsiang-ChehHuang.html
http___dx.doi.org_10.1109_LifeTech53646.2022.9754834 http://dx.doi.org/10.1109/LifeTech53646.2022.9754834
Chang:2010:WSEAS Load Identification of Non-intrusive Load-monitoring System in Smart Home
Hsueh-HsienChang.html
http___citeseerx.ist.psu.edu_viewdoc_summary_doi_10.1.1.455.3952 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.455.3952
http___www.worldses.org_journals_systems_systems-2010.htm http://www.worldses.org/journals/systems/systems-2010.htm
http___www.wseas.us_e-library_transactions_systems_2010_42-415.pdf http://www.wseas.us/e-library/transactions/systems/2010/42-415.pdf
Chang:2010:ICEBE A New Method for Load Identification of Nonintrusive Energy Management System in Smart Home
Hsueh-HsienChang.html
Ching-LungLin.html
http___dx.doi.org_10.1109_ICEBE.2010.24 http://dx.doi.org/10.1109/ICEBE.2010.24
Chang:2008:ICNC Rutting Prediction Model Developed by Genetic Programming Method Through Full Scale Accelerated Pavement Testing
Jia-RueyChang.html
Shun-HsingChen.html
Dar-HaoChen.html
Yao-BinLiu.html
http___dx.doi.org_10.1109_ICNC.2008.673 http://dx.doi.org/10.1109/ICNC.2008.673
Chang:2010:ICNC Pavement maintenance and rehabilitation decisions derived by genetic programming
Jia-RueyChang.html
Sao-JengChao.html
http___dx.doi.org_10.1109_ICNC.2010.5583502 http://dx.doi.org/10.1109/ICNC.2010.5583502
Chang:2020:AIdrug Swarm and Evolutionary Intelligence
MarkChang.html
https___www.taylorfrancis.com_books_mono_10.1201_9780429345159_artificial-intelligence-drug-development-precision-medicine-healthcare-mark-chang https://www.taylorfrancis.com/books/mono/10.1201/9780429345159/artificial-intelligence-drug-development-precision-medicine-healthcare-mark-chang
https___www.amazon.co.uk_Artificial-Intelligence-Development-Healthcare-Biostatistics_dp_0367362929 https://www.amazon.co.uk/Artificial-Intelligence-Development-Healthcare-Biostatistics/dp/0367362929
http___dx.doi.org_10.1201_9780429345159 http://dx.doi.org/10.1201/9780429345159
Chang:2013:SMC Comparative Data Fusion between Genetic Programing and Neural Network Models for Remote Sensing Images of Water Quality Monitoring
Ni-BinChang.html
BenjaminWVannah.html
http___dx.doi.org_10.1109_SMC.2013.182 http://dx.doi.org/10.1109/SMC.2013.182
Chang:2013:ieeeICNSCerie Intercomparisons between empirical models with data fusion techniques for monitoring water quality in a large lake
Ni-BinChang.html
BenjaminWVannah.html
http___dx.doi.org_10.1109_ICNSC.2013.6548747 http://dx.doi.org/10.1109/ICNSC.2013.6548747
Chang:2013:ieeeICNSCtampa Monitoring nutrient concentrations in Tampa Bay with MODIS images and machine learning models
Ni-BinChang.html
ZheminXuan.html
http___dx.doi.org_10.1109_ICNSC.2013.6548824 http://dx.doi.org/10.1109/ICNSC.2013.6548824
Chang:2013:RSE Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with MODIS images and machine learning models
Ni-BinChang.html
ZheminXuan.html
YJeffreyYang.html
http___dx.doi.org_10.1016_j.rse.2013.03.002 http://dx.doi.org/10.1016/j.rse.2013.03.002
http___www.sciencedirect.com_science_article_pii_S0034425713000746 http://www.sciencedirect.com/science/article/pii/S0034425713000746
Chang:2014:ieeeSTAEORS Comparative Sensor Fusion Between Hyperspectral and Multispectral Satellite Sensors for Monitoring Microcystin Distribution in Lake Erie
Ni-BinChang.html
BenjaminWVannah.html
YJeffreyYang.html
http___dx.doi.org_10.1109_JSTARS.2014.2329913 http://dx.doi.org/10.1109/JSTARS.2014.2329913
Chang:2015:ICNSC Improving the control of water treatment plant with remote sensing-based water quality forecasting model
Ni-BinChang.html
SanazImen.html
http___dx.doi.org_10.1109_ICNSC.2015.7116009 http://dx.doi.org/10.1109/ICNSC.2015.7116009
Chang:2015:EI Diagnosis of the artificial intelligence-based predictions of flow regime in a constructed wetland for stormwater pollution control
Ni-BinChang.html
GolamMohiuddin.html
AJamesCrawford.html
KaixuBai.html
Kang-RenJin.html
http___dx.doi.org_10.1016_j.ecoinf.2015.05.001 http://dx.doi.org/10.1016/j.ecoinf.2015.05.001
http___www.sciencedirect.com_science_article_pii_S1574954115000795 http://www.sciencedirect.com/science/article/pii/S1574954115000795
Chang:2006:mej Automated synthesis of passive filter circuits including parasitic effects by genetic programming
Shoou-JinnChang.html
Hao-ShengHou.html
Yan-KuinSu.html
http___dx.doi.org_10.1016_j.mejo.2005.12.012 http://dx.doi.org/10.1016/j.mejo.2005.12.012
Chang:2005:FGCS Nonlinear model for ECG R-R interval variation using genetic programming approach
YunSeokChang.html
KwangSukPark.html
BoYeonKim.html
http___www.sciencedirect.com_science_article_B6V06-4CVX0RT-1_2_111fea795562435e39023c448749d96a http://www.sciencedirect.com/science/article/B6V06-4CVX0RT-1/2/111fea795562435e39023c448749d96a
http___dx.doi.org_10.1016_j.future.2004.03.011 http://dx.doi.org/10.1016/j.future.2004.03.011
CHS06 Automated passive filter synthesis using a novel tree representation and genetic programming
Shoou-JinnChang.html
Hao-ShengHou.html
Yan-KuinSu.html
http___dx.doi.org_10.1109_TEVC.2005.861415 http://dx.doi.org/10.1109/TEVC.2005.861415
WeiChang:2011:TMEE Prediction of dissolved gas Content in transformer oil based on Genetic Programming and DGA
WeiChang.html
NingHao.html
http___dx.doi.org_10.1109_TMEE.2011.6199404 http://dx.doi.org/10.1109/TMEE.2011.6199404
Channon:masters The Evolutionary Emergence route to Artificial Intelligence
AlastairDChannon.html
http___www.channon.net_alastair_msc_adc_msc.pdf http://www.channon.net/alastair/msc/adc_msc.pdf
ChaDam97 The Artificial Evolution of Real Intelligence by Natural Selection
AlastairDChannon.html
RobertIDamper.html
http___www.channon.net_alastair_geb_ecal1997_channon_ad_ecal97.pdf http://www.channon.net/alastair/geb/ecal1997/channon_ad_ecal97.pdf
ALIFE98*384 Evolving Novel Behaviors via Natural Selection
AlastairDChannon.html
RobertIDamper.html
http___www.channon.net_alastair_geb_alife6_channon_ad_alife6.pdf http://www.channon.net/alastair/geb/alife6/channon_ad_alife6.pdf
Channon_sab98 Perpetuating evolutionary emergence
AlastairDChannon.html
RobertIDamper.html
http___www.channon.net_alastair_geb_sab98_channon_ad_sab98_nc.pdf http://www.channon.net/alastair/geb/sab98/channon_ad_sab98_nc.pdf
http___eprints.soton.ac.uk_id_eprint_250478 http://eprints.soton.ac.uk/id/eprint/250478
https___ieeexplore.ieee.org_document_6278697 https://ieeexplore.ieee.org/document/6278697
http___dx.doi.org_10.7551_mitpress_3119.001.0001 http://dx.doi.org/10.7551/mitpress/3119.001.0001
ChaDam00 Towards the evolutionary emergence of increasingly complex advantageous behaviours
AlastairDChannon.html
RobertIDamper.html
http___www.channon.net_alastair_geb_ijssepcs_channon_ad_ijssepcs.pdf http://www.channon.net/alastair/geb/ijssepcs/channon_ad_ijssepcs.pdf
http___dx.doi.org_10.1080_002077200406570 http://dx.doi.org/10.1080/002077200406570
channon_ad_phdthesis Evolutionary Emergence: The Struggle for Existence in Artificial Biota
AlastairDChannon.html
http___www.channon.net_alastair_geb_phdthesis_channon_ad_phdthesis.pdf http://www.channon.net/alastair/geb/phdthesis/channon_ad_phdthesis.pdf
Channon:2001:PAT Passing the ALife Test: Activity Statistics Classify Evolution in Geb as Unbounded
AlastairDChannon.html
http___www.channon.net_alastair_geb_ecal2001_channon_ad_ecal2001.pdf http://www.channon.net/alastair/geb/ecal2001/channon_ad_ecal2001.pdf
http___dx.doi.org_10.1007_3-540-44811-X_45 http://dx.doi.org/10.1007/3-540-44811-X_45
Channon:2002:alife Improving and still passing the ALife test: Component-normalised activity statistics classify evolution in Geb as unbounded
AlastairDChannon.html
http___www.channon.net_alastair_geb_alife8_channon_ad_alife8.pdf http://www.channon.net/alastair/geb/alife8/channon_ad_alife8.pdf
http___www.alife.org_alife8_proceedings_sub2118.pdf http://www.alife.org/alife8/proceedings/sub2118.pdf
Chapelle:2000:isr Genetic programming for inverse kinematics approximation
FredericChapelle.html
OChocron.html
PhilippeBidaud.html
http___books.google.co.uk_books_about_ISR_2000.html_id_u6zpAAAAMAAJ_redir_esc_y http://books.google.co.uk/books/about/ISR_2000.html?id=u6zpAAAAMAAJ&redir_esc=y
Chapelle:2001:icra A closed form for inverse kinematics approximation of general 6R manipulators using genetic programming
FredericChapelle.html
PhilippeBidaud.html
http___dx.doi.org_10.1109_ROBOT.2001.933137 http://dx.doi.org/10.1109/ROBOT.2001.933137
Chapelle:2002:thesis Evaluation de systemes robotiques et comportements complexes par algorithmes evolutionnaires
FredericChapelle.html
http___www.sudoc.fr_069898715 http://www.sudoc.fr/069898715
Chapelle:2002:jrtpm Conception et evaluation de micro-endoscopes basees sur les algorithmes evolutionnaires
FredericChapelle.html
PhilippeBidaud.html
GeorgesDumont.html
Chapelle:2004:MMT Closed form solutions for inverse kinematics approximation of general 6R manipulators
FredericChapelle.html
PhilippeBidaud.html
http___www.sciencedirect.com_science_article_B6V46-4B1XNXT-1_2_2bf40af1f930c87f19d6fcc130f2f57a http://www.sciencedirect.com/science/article/B6V46-4B1XNXT-1/2/2bf40af1f930c87f19d6fcc130f2f57a
http___dx.doi.org_10.1016_j.mechmachtheory.2003.09.003 http://dx.doi.org/10.1016/j.mechmachtheory.2003.09.003
Chapelle:2006:MMT Evaluation functions synthesis for optimal design of hyper-redundant robotic systems
FredericChapelle.html
PhilippeBidaud.html
http___dx.doi.org_10.1016_j.mechmachtheory.2005.11.006 http://dx.doi.org/10.1016/j.mechmachtheory.2005.11.006
char:1997:caiGP Constructivist AI with GP
KGovindaChar.html
char:1997:elGPcAI Evolution of Learning with Genetic Programming - Constructivist AI with Genetic Programming
KGovindaChar.html
Char:1997:HEC Pattern recognition
KGovindaChar.html
WalterAldenTackett.html
http___citeseerx.ist.psu.edu_viewdoc_download_doi_10.1.1.375.6494.pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf
http___www.crcnetbase.com_isbn_9780750308953 http://www.crcnetbase.com/isbn/9780750308953
char:1998:clGP Constructive Learning with Genetic Programming
KGovindaChar.html
http___www.cs.ucl.ac.uk_staff_W.Langdon_ftp_papers_csrp-98-10.pdf http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf
char:thesis Constructivist Artificial Intelligence with Genetic Programming
KGovindaChar.html
http___ethos.bl.uk_OrderDetails.do_did_10_uin_uk.bl.ethos.265641 http://ethos.bl.uk/OrderDetails.do?did=10&uin=uk.bl.ethos.265641
Chareka:2016:PRASA A study of fitness functions for data classification using grammatical evolution
TatendaChareka.html
NelishiaPillay.html
http___dx.doi.org_10.1109_RoboMech.2016.7813165 http://dx.doi.org/10.1109/RoboMech.2016.7813165
Charhate:2007:JEME Soft and hard computing approaches for real-time prediction of currents in a tide-dominated coastal area
SBCharhate.html
MCDeo.html
VSanilKumar.html
http___dx.doi.org_10.1243_14750902JEME77 http://dx.doi.org/10.1243/14750902JEME77
Charhate2008120 Inverse modeling to derive wind parameters from wave measurements
SBCharhate.html
MCDeo.html
SNLondhe.html
http___dx.doi.org_10.1016_j.apor.2008.08.002 http://dx.doi.org/10.1016/j.apor.2008.08.002
http___www.sciencedirect.com_science_article_B6V1V-4TCGM50-1_2_69dcf477c9fc85235d0cc5df25e6a54a http://www.sciencedirect.com/science/article/B6V1V-4TCGM50-1/2/69dcf477c9fc85235d0cc5df25e6a54a
Charhate:thesis Applications of soft computing techniques to solve coastal and ocean problems
SBCharhate.html
http___www.civil.iitb.ac.in__mcdeo_thesis.html http://www.civil.iitb.ac.in/~mcdeo/thesis.html
Charhate200929 Genetic Programming to Forecast Stream Flow
SBCharhate.html
YHDandawat.html
SNLondhe.html
http___dx.doi.org_10.1007_978-3-540-89465-0_6 http://dx.doi.org/10.1007/978-3-540-89465-0_6
http___dx.doi.org_10.1007_978-3-540-89465-0_6 http://dx.doi.org/10.1007/978-3-540-89465-0_6
Charhate:2009:SOS Genetic programming for real-time prediction of offshore wind
SBCharhate.html
MCDeo.html
SNLondhe.html
http___dx.doi.org_10.1080_17445300802492638 http://dx.doi.org/10.1080/17445300802492638
chatterjee:2022:JBSMSE Prediction of welding responses using AI approach: adaptive neuro-fuzzy inference system and genetic programming
SumanChatterjee.html
SibaSankarMahapatra.html
LucianoLamberti.html
CatalinIPruncu.html
http___link.springer.com_article_10.1007_s40430-021-03294-w http://link.springer.com/article/10.1007/s40430-021-03294-w
http___dx.doi.org_10.1007_s40430-021-03294-w http://dx.doi.org/10.1007/s40430-021-03294-w
chattoe:1998:uEArsp Just How (Un)realistic are Evolutionary Algorithms as Representations of Social Processes?
EdmundChattoe-Brown.html
http___jasss.soc.surrey.ac.uk_1_3_2.html http://jasss.soc.surrey.ac.uk/1/3/2.html
Chattoe:2001:OundG The Prospects for Artificial Intelligence Techniques in Understanding Economic Behaviour: An Overview
EdmundChattoe-Brown.html
http___www.metropolis-publisher.com_Komplexitaet-und-Lernen_997_book.do http://www.metropolis-publisher.com/Komplexitaet-und-Lernen/997/book.do
http___www.amazon.de__C3_96konomie-Gesellschaft-Jahrb-17-Komplexit_C3_A4t-Lernen_dp_3895189979 http://www.amazon.de/%C3%96konomie-Gesellschaft-Jahrb-17-Komplexit%C3%A4t-Lernen/dp/3895189979
Chattoe:thesis The Evolution of Expectations in Boundedly Rational Agents
EdmundChattoe-Brown.html
https___www.academia.edu_8906840_The_Evolution_of_Expectations_in_Boundedly_Rational_Agents_Front_Material https://www.academia.edu/8906840/The_Evolution_of_Expectations_in_Boundedly_Rational_Agents_Front_Material
chattoe:2004:gagpf Genetic Algorithms and Genetic Programming in Computational Finance, Chen, Shu-Heng (ed.)
EdmundChattoe-Brown.html
http___jasss.soc.surrey.ac.uk_7_4_reviews_chattoe.html http://jasss.soc.surrey.ac.uk/7/4/reviews/chattoe.html
Chattoe-Brown:2013:SSC Modelling Evolutionary Mechanisms in Social Systems
EdmundChattoe-Brown.html
BruceEdmonds.html
http___www.springer.com_computer_information_systems_and_applications_book_978-3-540-93812-5 http://www.springer.com/computer/information+systems+and+applications/book/978-3-540-93812-5
Chaturvedi:2020:CEC Genetic Programming for Domain Adaptation in Product Reviews
ItiChaturvedi.html
ErikCambria.html
SandroCavallari.html
RoyEWelsch.html
http___vigir.missouri.edu__gdesouza_Research_Conference_CDs_IEEE_WCCI_2020_CEC_Papers_E-24673.pdf http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_WCCI_2020/CEC/Papers/E-24673.pdf
http___dx.doi.org_10.1109_CEC48606.2020.9185713 http://dx.doi.org/10.1109/CEC48606.2020.9185713
Chaturvedi:2021:CognComput Fuzzy Aggregated Topology Evolution for Cognitive Multi-tasks
ItiChaturvedi.html
ChitLSu.html
RoyEWelsch.html
https___hdl.handle.net_1721.1_131981 https://hdl.handle.net/1721.1/131981
http___dx.doi.org_10.1007_s12559-020-09807-4 http://dx.doi.org/10.1007/s12559-020-09807-4
DBLP:conf/icarcv/ChaudhariPT08 A multiclass classifier using Genetic Programming
NarendraSChaudhari.html
AnuradhaPurohit.html
ArunaTiwari.html
http___dx.doi.org_10.1109_ICARCV.2008.4795815 http://dx.doi.org/10.1109/ICARCV.2008.4795815
Chaudhari:2012:IJHST Estimation of pan evaporation using soft computing tools
NarhariChaudhari.html
SNLondhe.html
KCKhare.html
http___www.inderscience.com_link.php_id_52375 http://www.inderscience.com/link.php?id=52375
http___dx.doi.org_10.1504_IJHST.2012.052375 http://dx.doi.org/10.1504/IJHST.2012.052375
Chaudhari:2015:IJHST Spatial mapping of pan evaporation using linear genetic programming
NarhariChaudhari.html
SNLondhe.html
KCKhare.html
http___www.inderscience.com_link.php_id_67731 http://www.inderscience.com/link.php?id=67731
http___dx.doi.org_10.1504_IJHST.2014.067731 http://dx.doi.org/10.1504/IJHST.2014.067731
chaudhari:2016:FICSCPS One Day Ahead Forecast of Pan Evaporation at Pali Using Genetic Programming
NarhariChaudhari.html
NehaNarhariChaudhari.html
http___link.springer.com_chapter_10.1007_978-981-10-0448-3_10 http://link.springer.com/chapter/10.1007/978-981-10-0448-3_10
http___dx.doi.org_10.1007_978-981-10-0448-3_10 http://dx.doi.org/10.1007/978-981-10-0448-3_10
Chaudhary:2009:INMIC Determination of optimum genetic parameters for symbolic non-linear regression-like problems in genetic programming
UKChaudhary.html
MIqbal.html
http___dx.doi.org_10.1109_INMIC.2009.5383162 http://dx.doi.org/10.1109/INMIC.2009.5383162
Chaudhri:2000:GECCO Characterizing a Tunably Difficult Problem in Genetic Programming
OmarAChaudhri.html
JasonMDaida.html
JonathanCKhoo.html
WendellSRichardson.html
RachelBHarrison.html
WilliamJSloat.html
http___gpbib.cs.ucl.ac.uk_gecco2000_GP206.pdf http://gpbib.cs.ucl.ac.uk/gecco2000/GP206.pdf
Chaudhry:thesis Image Restoration using Machine Learning
AsmatullahChaudhry.html
https___fac.ksu.edu.sa_ammirza_page_22439 https://fac.ksu.edu.sa/ammirza/page/22439
http___prr.hec.gov.pk_jspui_handle_123456789_4816 http://prr.hec.gov.pk/jspui/handle/123456789/4816
http___prr.hec.gov.pk_thesis_2056.pdf http://prr.hec.gov.pk/thesis/2056.pdf
Chaudhry:2007:IJIST A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming
AsmatullahChaudhry.html
AsifullahKhan.html
AsadAli.html
AnwarMMirza.html
http___dx.doi.org_10.1002_ima.20105 http://dx.doi.org/10.1002/ima.20105
Chaudhry:2009:murjet Fusion of Linear and Non-Linear Image Restoration Filters Using Genetic Programming
AsmatullahChaudhry.html
AnwarMMirza.html
NisarAhmedMemon.html
http___direct.bl.uk_bld_OrderDetails.do_ http://direct.bl.uk/bld/OrderDetails.do?
Chauhan:2020:ICIMIA Automated Machine Learning: The New Wave of Machine Learning
KaransinghChauhan.html
ShreenaJani.html
DhruminThakkar.html
RiddhamDave.html
JitendraBBhatia.html
SudeepTanwar.html
MohammadSObaidat.html
http___dx.doi.org_10.1109_ICIMIA48430.2020.9074859 http://dx.doi.org/10.1109/ICIMIA48430.2020.9074859
Chaumont:2016:GPEM Evolution of sustained foraging in three-dimensional environments with physics
NicolasChaumont.html
ChristophAdami.html
http___dx.doi.org_10.1007_s10710-016-9270-z http://dx.doi.org/10.1007/s10710-016-9270-z
Chavan:2013:IJERA Shear Strength of Slender Reinforced Concrete Beams without Web Reinforcement
RSChavan.html
PrashantMPawar.html
https___www.ijera.com_pages_v3-no6.html https://www.ijera.com/pages/v3-no6.html
https___www.ijera.com_papers_Vol3_issue6_CQ36554559.pdf https://www.ijera.com/papers/Vol3_issue6/CQ36554559.pdf
chavez:2007:MAEB Una Herramienta de Programacion Genetica Paralela que Aprovecha Recursos Publicos de Computacion
FranciscoChavezdelaO.html
JoseLuisGuisado.html
DanielLombranaGonzalezRodriguez.html
FranciscoFernandezdeVega.html
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Chavez:2018:GECCOcomp Energy-consumption prediction of genetic programming algorithms using a fuzzy rule-based system
FranciscoChavezdelaO.html
FranciscoFernandezdeVega.html
JosefaDiazAlvarez.html
JoseAntonioGarciaMunoz.html
FranciscoJavierRodriguezDiaz.html
PedroACastilloValdivieso.html
http___dx.doi.org_10.1145_3205651.3208216 http://dx.doi.org/10.1145/3205651.3208216
Chavoya:2011:ITNG Applying Genetic Programming for Estimating Software Development Effort of Short-scale Projects
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Chavoya:2012:PLOS Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects
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Chavoya:2013:IJACSA Software Development Effort Estimation by Means of Genetic Programming
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MariaElenaMeda-Campana.html
http___thesai.org_Downloads_Volume4No11_Paper_15-Software_Development_Effort_Estimation_by_Means_of_Genetic_Programming.pdf http://thesai.org/Downloads/Volume4No11/Paper_15-Software_Development_Effort_Estimation_by_Means_of_Genetic_Programming.pdf
http___dx.doi.org_10.14569_IJACSA.2013.041115 http://dx.doi.org/10.14569/IJACSA.2013.041115
http___dx.doi.org_10.14569_IJACSA.2013.041115 http://dx.doi.org/10.14569/IJACSA.2013.041115
cheang2:2003:gecco Data Classification Using Genetic Parallel Programming
IvanSinManCheang.html
Kin-HongLee.html
Kwong-SakLeung.html
http___dx.doi.org_10.1007_3-540-45110-2_88 http://dx.doi.org/10.1007/3-540-45110-2_88
cheang:2003:gecco Improving Evolvability of Genetic Parallel Programming Using Dynamic Sample Weighting
IvanSinManCheang.html
Kin-HongLee.html
Kwong-SakLeung.html
http___dx.doi.org_10.1007_3-540-45110-2_72 http://dx.doi.org/10.1007/3-540-45110-2_72
cheang:gecco03lbp An Empirical Study of the Accelerating Phenomenon in Genetic Parallel Programming
IvanSinManCheang.html
Kin-HongLee.html
Kwong-SakLeung.html
cheang:2003:edcpugpp Evolving data classification programs using genetic parallel programming
IvanSinManCheang.html
Kin-HongLee.html
Kwong-SakLeung.html
http___dx.doi.org_10.1109_CEC.2003.1299582 http://dx.doi.org/10.1109/CEC.2003.1299582
Man:2003:Aswmtgpp Applying sample weighting methods to genetic parallel programming
IvanSinManCheang.html
Kin-HongLee.html
Kwong-SakLeung.html
http___dx.doi.org_10.1109_CEC.2003.1299766 http://dx.doi.org/10.1109/CEC.2003.1299766
cheang:2003:CIRAS An Empirical Study of the GPP Accelerating Phenomenon
IvanSinManCheang.html
cheang:2004:eurogp Designing Optimal Combinational Digital Circuits Using a Multiple Logic Unit Processor
IvanSinManCheang.html
Kin-HongLee.html
Kwong-SakLeung.html
http___dx.doi.org_10.1007_978-3-540-24650-3_3 http://dx.doi.org/10.1007/978-3-540-24650-3_3
cheang:thesis Genetic parallel programming
IvanSinManCheang.html
https___search.proquest.com_docview_305346245 https://search.proquest.com/docview/305346245
Cheang:2006:EC Genetic Parallel Programming: Design and Implementation
IvanSinManCheang.html
Kwong-SakLeung.html
Kin-HongLee.html
http___dx.doi.org_10.1162_evco.2006.14.2.129 http://dx.doi.org/10.1162/evco.2006.14.2.129
Cheang:2007:tec Applying Genetic Parallel Programming to Synthesize Combinational Logic Circuits
IvanSinManCheang.html
Kin-HongLee.html
Kwong-SakLeung.html
http___dx.doi.org_10.1109_TEVC.2006.884044 http://dx.doi.org/10.1109/TEVC.2006.884044
Chee:2014:ROMA Using a co-evolutionary approach to automatically generate vertical undulation and lateral rolling motions for snake-like modular robot
WeiShunChee.html
JasonTeo.html
http___dx.doi.org_10.1109_ROMA.2014.7295894 http://dx.doi.org/10.1109/ROMA.2014.7295894
Chee:2014:ICAIET Simultaneous Evolutionary-Based Optimization of Controller and Morphology of Snake-Like Modular Robots
WeiShunChee.html
JasonTeo.html
http___dx.doi.org_10.1109_ICAIET.2014.16 http://dx.doi.org/10.1109/ICAIET.2014.16
cheema:2002:BTP Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation
JitenderJitSinghCheema.html
NarendraVSankpal.html
SanjeevSTambe.html
BhaskarDKulkarni.html
http___www3.interscience.wiley.com_journal_121399381_abstract http://www3.interscience.wiley.com/journal/121399381/abstract
http___dx.doi.org_10.1021_bp015509s http://dx.doi.org/10.1021/bp015509s
chellapilla:1998:agnoclbbEP Automatic Generation of Nonlinear Optimal Control Laws for Broom Balancing using Evolutionary Programming
KumarChellapilla.html
http___dx.doi.org_10.1109_ICEC.1998.699500 http://dx.doi.org/10.1109/ICEC.1998.699500
chellapilla:1998:piempwsx A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover
KumarChellapilla.html
http___www.cs.ucl.ac.uk_staff_W.Langdon_ftp_papers_gp1998_chellapilla_1998_piempwsx.pdf http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/chellapilla_1998_piempwsx.pdf
Chellapilla:1998:eptm Evolving Computer Programs without Subtree Crossover
KumarChellapilla.html
http___dx.doi.org_10.1109_4235.661552 http://dx.doi.org/10.1109/4235.661552
Chellapilla:thesis Designing Effective Evolutionary Computations
KumarChellapilla.html
http___search.proquest.com_docview_305003505 http://search.proquest.com/docview/305003505
Chen:2014:ICNC Applications of artificial intelligence technologies in credit scoring: A survey of literature
BiliChen.html
WenhuaZeng.html
YangbinLin.html
http___dx.doi.org_10.1109_ICNC.2014.6975914 http://dx.doi.org/10.1109/ICNC.2014.6975914
Chen:2009:CINC A Self-adapting Algorithm for Identifying Rheology Model and Its Parameters of Rock Mass
Bing-RuiChen.html
Xia-TingFeng.html
ChengxiangYang.html
http___dx.doi.org_10.1109_CINC.2009.39 http://dx.doi.org/10.1109/CINC.2009.39
Carla_Chen_Thesis Bayesian methodology for genetics of complex diseases
CarlaChia-MingChen.html
http___eprints.qut.edu.au_43357_ http://eprints.qut.edu.au/43357/
http___eprints.qut.edu.au_43357_1_Carla_Chen_Thesis.pdf http://eprints.qut.edu.au/43357/1/Carla_Chen_Thesis.pdf
Chen:2011:TCBB Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression
CarlaChia-MingChen.html
HolgerSchwender.html
JonathanKeith.html
RobinNunkesser.html
KerrieMengersen.html
PaulaMacrossan.html
http___dx.doi.org_10.1109_TCBB.2011.46 http://dx.doi.org/10.1109/TCBB.2011.46
Chen:2013:JH Typhoon event-based evolutionary fuzzy inference model for flood stage forecasting
Chang-ShianChen.html
You-DaJhong.html
Ting-YingWu.html
Shien-TsungChen.html
http___dx.doi.org_10.1016_j.jhydrol.2013.03.033 http://dx.doi.org/10.1016/j.jhydrol.2013.03.033
http___www.sciencedirect.com_science_article_pii_S0022169413002424 http://www.sciencedirect.com/science/article/pii/S0022169413002424
Chen:2017:ICNC-FSKD Elite bases regression: A real-time algorithm for symbolic regression
ChenChen.html
ChangtongLuo.html
ZonglinJiang.html
http___dx.doi.org_10.1109_FSKD.2017.8393325 http://dx.doi.org/10.1109/FSKD.2017.8393325
Chen:2017:ISCID Fast Modeling Methods for Complex System with Separable Features
ChenChen.html
ChangtongLuo.html
ZonglinJiang.html
http___dx.doi.org_10.1109_ISCID.2017.144 http://dx.doi.org/10.1109/ISCID.2017.144
CHEN20181973 Block building programming for symbolic regression
ChenChen.html
ChangtongLuo.html
ZonglinJiang.html
http___www.sciencedirect.com_science_article_pii_S0925231217316983 http://www.sciencedirect.com/science/article/pii/S0925231217316983
http___dx.doi.org_10.1016_j.neucom.2017.10.047 http://dx.doi.org/10.1016/j.neucom.2017.10.047
CHEN:2018:ESA A multilevel block building algorithm for fast modeling generalized separable systems
ChenChen.html
ChangtongLuo.html
ZonglinJiang.html
http___dx.doi.org_10.1016_j.eswa.2018.05.021 http://dx.doi.org/10.1016/j.eswa.2018.05.021
http___www.sciencedirect.com_science_article_pii_S0957417418303142 http://www.sciencedirect.com/science/article/pii/S0957417418303142
chen:2009:CRTMCDM Nonlinear Deterministic Frontier Model Using Genetic Programming
Chin-YiChen.html
Jih-JengHuang.html
Gwo-HshiungTzeng.html
http___link.springer.com_chapter_10.1007_978-3-642-02298-2_111 http://link.springer.com/chapter/10.1007/978-3-642-02298-2_111
http___dx.doi.org_10.1007_978-3-642-02298-2_111 http://dx.doi.org/10.1007/978-3-642-02298-2_111
chen:2009:SMC Network intrusion detection using fuzzy class association rule mining based on genetic network programming
CiChen.html
ShingoMabu.html
ChuanYue.html
KaoruShimada.html
KotaroHirasawa.html
http___dx.doi.org_10.1109_ICSMC.2009.5346328 http://dx.doi.org/10.1109/ICSMC.2009.5346328
Chen2009634 A new variable topology for evolutionary hardware design
Chih-YungChen.html
Rey-ChueHwang.html
http___dx.doi.org_10.1016_j.eswa.2007.09.017 http://dx.doi.org/10.1016/j.eswa.2007.09.017
http___www.sciencedirect.com_science_article_B6V03-4PV2RVX-6_2_6aa751f84c76e323ab6ddab36f70e63d http://www.sciencedirect.com/science/article/B6V03-4PV2RVX-6/2/6aa751f84c76e323ab6ddab36f70e63d
CHEN:2020:Measurement Railway turnout system RUL prediction based on feature fusion and genetic programming
CongChen.html
TianhuaXu.html
GuangWang.html
BoLi.html
http___dx.doi.org_10.1016_j.measurement.2019.107162 http://dx.doi.org/10.1016/j.measurement.2019.107162
http___www.sciencedirect.com_science_article_pii_S0263224119310280 http://www.sciencedirect.com/science/article/pii/S0263224119310280
CHEN:2023:swevo A guided genetic programming with attribute node activation encoding for resource constrained project scheduling problem
HaojieChen.html
XinyuLi.html
LiangGao.html
http___dx.doi.org_10.1016_j.swevo.2023.101418 http://dx.doi.org/10.1016/j.swevo.2023.101418
https___www.sciencedirect.com_science_article_pii_S2210650223001918 https://www.sciencedirect.com/science/article/pii/S2210650223001918
CHEN:2024:mtcomm Size-dependent nonlinear vibrations of functionally graded origami-enabled auxetic metamaterial plate: Application of artificial intelligence networks for solving the engineering problem
FenghuaChen.html
XinguoQiu.html
KhalidAAlnowibet.html
http___dx.doi.org_10.1016_j.mtcomm.2024.108232 http://dx.doi.org/10.1016/j.mtcomm.2024.108232
https___www.sciencedirect.com_science_article_pii_S2352492824002125 https://www.sciencedirect.com/science/article/pii/S2352492824002125
conf/ausai/ChenZ05 Evolving While-Loop Structures in Genetic Programming for Factorial and Ant Problems
GuangChen.html
MengjieZhang.html
https___rdcu.be_dgJfN https://rdcu.be/dgJfN
http___dx.doi.org_10.1007_11589990_144 http://dx.doi.org/10.1007/11589990_144
Chen:2020:ACC A Genetic Programming-Driven Data Fitting Method
HaoChen.html
ZiYuanGuo.html
HongBaiDuan.html
DuoBan.html
http___dx.doi.org_10.1109_ACCESS.2020.3002563 http://dx.doi.org/10.1109/ACCESS.2020.3002563
CHEN:2022:asoc A two-stage genetic programming framework for Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions
HaoJieChen.html
JianZhang2.html
RongLi.html
GuofuDing.html
Sheng-fengQin.html
http___dx.doi.org_10.1016_j.asoc.2022.109087 http://dx.doi.org/10.1016/j.asoc.2022.109087
https___www.sciencedirect.com_science_article_pii_S1568494622003751 https://www.sciencedirect.com/science/article/pii/S1568494622003751
CHEN:2022:eswa A filtering genetic programming framework for stochastic resource constrained multi-project scheduling problem under new project insertions
HaoJieChen.html
GuofuDing.html
JianZhang2.html
RongLi.html
LeiJiang.html
Sheng-fengQin.html
http___dx.doi.org_10.1016_j.eswa.2022.116911 http://dx.doi.org/10.1016/j.eswa.2022.116911
https___www.sciencedirect.com_science_article_pii_S0957417422003487 https://www.sciencedirect.com/science/article/pii/S0957417422003487
CHEN:2021:ESA A hyper-heuristic based ensemble genetic programming approach for stochastic resource constrained project scheduling problem
HaoJieChen.html
GuofuDing.html
Sheng-fengQin.html
JianZhang2.html
http___dx.doi.org_10.1016_j.eswa.2020.114174 http://dx.doi.org/10.1016/j.eswa.2020.114174
https___www.sciencedirect.com_science_article_pii_S0957417420309118 https://www.sciencedirect.com/science/article/pii/S0957417420309118
Chen:mastersthesis Genetic Programming for the Investment of the Mutual Fund with Sortino Ratio and Mean Variance Model
Hung-HsinChen.html
http___etd.lib.nsysu.edu.tw_ETD-db_ETD-search-c_view_etd_URN_etd-0824110-122030 http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0824110-122030
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journals/asc/ChenYP14 The trading on the mutual funds by gene expression programming with Sortino ratio
Hung-HsinChen.html
Chang-biauYang.html
Yung-HsingPeng.html
http___dx.doi.org_10.1016_j.asoc.2013.09.011 http://dx.doi.org/10.1016/j.asoc.2013.09.011
Chen2012 Genetic programming for predicting aseismic abilities of school buildings
Hung-MingChen2.html
Wei-KoKao.html
Hsing-ChihTsai.html
http___dx.doi.org_10.1016_j.engappai.2012.04.002 http://dx.doi.org/10.1016/j.engappai.2012.04.002
http___www.sciencedirect.com_science_article_pii_S0952197612000905 http://www.sciencedirect.com/science/article/pii/S0952197612000905
Jiah-ShingChen:2003:CINC Multi-Valued Stock Valuation Based on Fuzzy Genetic Programming Approach
Jiah-ShingChen.html
Ping-ChenLin.html
http___www.fin.kuas.edu.tw_people_writing_seminar.php_Sn_127 http://www.fin.kuas.edu.tw/people/writing_seminar.php?Sn=127
chen:2024:CEC2 Generate a Single Heuristic for Multiple Dynamic Flexible Job Shop Scheduling Tasks by Genetic Programming
JiayinChen.html
Ya-HuiJia.html
YingBi.html
Wei-NengChen.html
http___dx.doi.org_10.1109_CEC60901.2024.10611762 http://dx.doi.org/10.1109/CEC60901.2024.10611762
Chen:2016:JPDC enDebug: A hardware-software framework for automated energy debugging
JieChen.html
GuruPrasadhVenkataramani.html
http___dx.doi.org_10.1016_j.jpdc.2016.05.005 http://dx.doi.org/10.1016/j.jpdc.2016.05.005
http___www.sciencedirect.com_science_article_pii_S0743731516300351 http://www.sciencedirect.com/science/article/pii/S0743731516300351
Chen:2015:CECa A sensor tagging approach for reusing building blocks of knowledge in learning classifier systems
Liang-YuChen.html
Po-MingLee.html
Tzu-ChienHsiao.html
http___dx.doi.org_10.1109_CEC.2015.7257256 http://dx.doi.org/10.1109/CEC.2015.7257256
CHEN:2023:jmrt Development of predictive models for sustainable concrete via genetic programming-based algorithms
LinglingChen.html
ZhiyuanWang.html
AftabAhmadKhan.html
MajidKhan.html
MuhammadFaisalJaved.html
AbdulazizAlaskar.html
SayedMEldin.html
http___dx.doi.org_10.1016_j.jmrt.2023.04.180 http://dx.doi.org/10.1016/j.jmrt.2023.04.180
https___www.sciencedirect.com_science_article_pii_S223878542300875X https://www.sciencedirect.com/science/article/pii/S223878542300875X
conf/icnc/ChenC05b Dynamical Proportion Portfolio Insurance with Genetic Programming
Jiah-ShingChen.html
Chia-LanChang.html
http___dx.doi.org_10.1007_11539117_104 http://dx.doi.org/10.1007/11539117_104
Chen:2007:ESA Piecewise nonlinear goal-directed CPPI strategy
Jiah-ShingChen.html
BenjaminPenyangLiao.html
http___dx.doi.org_10.1016_j.eswa.2006.07.001 http://dx.doi.org/10.1016/j.eswa.2006.07.001
Chen2008273 Dynamic proportion portfolio insurance using genetic programming with principal component analysis
Jiah-ShingChen.html
Chia-LanChang.html
Jia-LiHou.html
Yao-TangLin.html
http___dx.doi.org_10.1016_j.eswa.2007.06.030 http://dx.doi.org/10.1016/j.eswa.2007.06.030
http___www.sciencedirect.com_science_article_B6V03-4P40KHS-4_2_0bbb6228d04a3a1a4d59108b17c37664 http://www.sciencedirect.com/science/article/B6V03-4P40KHS-4/2/0bbb6228d04a3a1a4d59108b17c37664
Chen:2016:YAC Displacement prediction model of landslide based on multi-gene genetic programming
JiejieChen.html
ZhigangZeng.html
PingJiang.html
http___dx.doi.org_10.1109_YAC.2016.7804942 http://dx.doi.org/10.1109/YAC.2016.7804942
journals/nca/ChenZJT16 Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction
JiejieChen.html
ZhigangZeng.html
PingJiang.html
HuimingTang.html
http___dx.doi.org_10.1007_s00521-015-1976-y http://dx.doi.org/10.1007/s00521-015-1976-y
conf/awic/ChenLW05 Distributed Service Management Based on Genetic Programming
JingChen.html
Zeng-zhiLi.html
Yun-LanWang.html
http___dx.doi.org_10.1007_11495772_14 http://dx.doi.org/10.1007/11495772_14
Chen:2005:ICMLC Distributed Service Performance Management Based on Linear Regression and Genetic Programming
JingChen.html
Zeng-zhiLi.html
Zhi-GangLiao.html
Yun-LanWang.html
http___dx.doi.org_10.1109_ICMLC.2005.1527007 http://dx.doi.org/10.1109/ICMLC.2005.1527007
chen:290 Study of Applying Macroevolutionary Genetic Programming to Concrete Strength Estimation
LiChen.html
http___link.aip.org_link__QCP_17_290_1 http://link.aip.org/link/?QCP/17/290/1
http___dx.doi.org_10.1061__ASCE_0887-3801_2003_17_4_290_ http://dx.doi.org/10.1061/(ASCE)0887-3801(2003)17:4(290)
Chen2008296 Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery
LiChen.html
Chih-HungTan.html
Shuh-JiKao.html
Tai-ShengWang.html
http___dx.doi.org_10.1016_j.watres.2007.07.014 http://dx.doi.org/10.1016/j.watres.2007.07.014
journals/ewc/Chen11 Macro-grammatical evolution for nonlinear time series modeling-a case study of reservoir inflow forecasting
LiChen.html
http___dx.doi.org_10.1007_s00366-011-0212-3 http://dx.doi.org/10.1007/s00366-011-0212-3
http___dx.doi.org_10.1007_s00366-011-0212-3 http://dx.doi.org/10.1007/s00366-011-0212-3
journals/eaai/ChenKM14 Prediction of slump flow of high-performance concrete via parallel hyper-cubic gene-expression programming
LiChen.html
Chang-HuanKou.html
Shih-WeiMa.html
http___dx.doi.org_10.1016_j.engappai.2014.05.005 http://dx.doi.org/10.1016/j.engappai.2014.05.005
Chen:2015:CECb An ant colony optimization-based hyper-heuristic with genetic programming approach for a hybrid flow shop scheduling problem
LinChen.html
HongZheng.html
DanZheng.html
DongniLi.html
http___dx.doi.org_10.1109_CEC.2015.7256975 http://dx.doi.org/10.1109/CEC.2015.7256975
journals/soco/ChenCCHH07 Comparing extended classifier system and genetic programming for financial forecasting: an empirical study
Mu-YenChen.html
Kuang-KuChen.html
Heien-KunChiang.html
Hwa-ShanHuang.html
Mu-JungHuang.html
http___dx.doi.org_10.1007_s00500-007-0161-3 http://dx.doi.org/10.1007/s00500-007-0161-3
WSEAS_466-157_Chen Automatic Running Planning for Omni-Directional Mobile Robot By Genetic Programming
PengChen.html
ShinjiKoyama.html
ShinichiroMitutake.html
TakashiIsoda.html
http___www.wseas.us_e-library_conferences_digest2003_papers_digest.htm http://www.wseas.us/e-library/conferences/digest2003/papers/digest.htm
http___www.wseas.us_e-library_conferences_digest2003_papers_466-157.pdf http://www.wseas.us/e-library/conferences/digest2003/papers/466-157.pdf
Chen:2005:MSSP Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming
PengChen.html
MasatoshiTaniguchi.html
ToshioToyota.html
ZhengjaHe.html
http___dx.doi.org_10.1016_j.ymssp.2003.11.004 http://dx.doi.org/10.1016/j.ymssp.2003.11.004
Chen:2011:IJCIS Automatic Design of Robust Optimal Controller for Interval Plants using Genetic Programming and Kharitonov Theorem
PengChen2.html
Yong-ZaiLu.html
http___citeseerx.ist.psu.edu_viewdoc_summary_doi_10.1.1.1010.701 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1010.701
http___atlantis-press.com_php_download_paper.php_id_3D2376 http://atlantis-press.com/php/download_paper.php?id%3D2376
http___www.tandfonline.com_doi_abs_10.1080_18756891.2011.9727834 http://www.tandfonline.com/doi/abs/10.1080/18756891.2011.9727834
http___dx.doi.org_10.1080_18756891.2011.9727834 http://dx.doi.org/10.1080/18756891.2011.9727834
Chen:2015:CEC Generalisation and Domain Adaptation in GP with Gradient Descent for Symbolic Regression
QiChen.html
BingXue.html
MengjieZhang.html
http___dx.doi.org_10.1109_CEC.2015.7257017 http://dx.doi.org/10.1109/CEC.2015.7257017
Chen:2016:GECCO Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation
QiChen.html
MengjieZhang.html
BingXue.html
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QiChen:thesis Improving the Generalisation of Genetic Programming for Symbolic Regression
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Chen:2001:ICCIMA1 Evolving Bargaining Strategies with Genetic Programming: An Overview of AIE-DA, Ver. 2, Part 1
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Chen:2001:ICCIMA2 Evolving Bargaining Strategies with Genetic Programming: An Overview of AIE-DA, Ver. 2, Part 2
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Shu-HengChen:2001:JEDC Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market
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Chen:2002:EJEMED Equilibrium Selection via Adaptation: Using Genetic Programming to Model Learning in a Coordination Game
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Chen:2002:JEBO On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis
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chen:2002:gagpcf Genetic Algorithms and Genetic Programming in Computational Finance
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ChenAO:2002:gagpcf An Overview
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ChenKuoShieh:2002:gagpcf Genetic Programming: A Tutorial With The Software Simple GP
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ChenLiao:2002:gagpcf Price Discovery in Agent-Based Computational Modeling of the Artificial Stock Market
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ChenTaiChie:2002:gagpcf Individual Rationality as a Partial Impediment to Market Efficiency: Allocative Efficiency of Markets with Smart Traders
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Chen:2003:AAAIs Economic Models of Innovations: Why GP Can Be a Possible Way Out?
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chen03 Overfitting or Poor Learning: A Critique of Current Financial Applications of GP
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Tzu-WenKuo.html
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Shu-HengChen:2003:CINC Modeling International Short-Term Capital Flow with Genetic Programming
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chen:2004:lbp Functional Modularity in the Test Bed of Economic Theory -- Using Genetic Programming
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Shu-HengChen:2004:IJMPB Functional Modularity in the Fundamentals of Economic Theory: Toward an Agent-Based Economic Modeling of the Evolution of Technology
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Bin-TzongChie.html
http___dx.doi.org_10.1142_S0217979204025403 http://dx.doi.org/10.1142/S0217979204025403
Chen:2005:IS Agent-based computational modeling of the stock price-volume relation
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Chen:2006:CNEI A Functional Modularity Approach to Agent-based Modeling of the Evolution of Technology
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Chen:2006:IS Computationally intelligent agents in economics and Finance
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conf/iconip/ChenN06 Pretests for Genetic-Programming Evolved Trading Programs: zero-intelligence Strategies and Lottery Trading
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NicolasNavet.html
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Chen:2007:IDEAL Modularity, Product Innovation, and Consumer Satisfaction: An Agent-Based Approach
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Chen:2007:chen Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms
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NicolasNavet.html
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Chen:2008:GPTP Co-Evolving Trading Strategies to Analyze Bounded Rationality in Double Auction Markets
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Chen:2009:CIFEr Modeling intelligence of learning agents in an artificial double auction market
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ChenZY:2009:GEC Analysis of micro-behavior and bounded rationality in double auction markets using co-evolutionary GP
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TinaYu.html
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conf/mabs/ChenTW09 Does Cognitive Capacity Matter When Learning Using Genetic Programming in Double Auction Markets?
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ShuGWang.html
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chen:2009:AAESCS Genetic Programming and Agent-Based Computational Economics: From Autonomous Agents to Product Innovation
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Chen:2010:maaECbit Bounded Rationality and Market Micro-Behaviors: Case Studies Based on Agent-Based Double Auction Markets
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Chen:2011:frontierEE Agents learned, but do we? Knowledge discovery using the agent-based double auction markets
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TinaYu.html
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conf/ideal/ChenS11 Is Genetic Programming ``Human-Competitive''? The Case of Experimental Double Auction Markets
Shu-HengChen.html
Kuo-ChuanShik.html
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Chen20121 Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective
Shu-HengChen.html
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chen:2022:FC Orientation Design and Research of Heavy Bamboo Substrate Considering Genetic Programming and Artificial Intelligence Algorithm
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CHEN:2021:CS Data-driven analysis on ultimate axial strain of FRP-confined concrete cylinders based on explicit and implicit algorithms
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Chen:2010:BIEF Macroeconomic Forecasting Using Genetic Programming Based Vector Error Correction Model
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Chen:2004:EAAI Multi-step optimal control of complex process: a genetic programming strategy and its application
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Chen:2014:OCMIEC Engineering Optimization Approaches of Nonferrous Metallurgical Processes
XiaofangChen.html
HongleiXu.html
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Chen:2008:ICNC Model of Water Production Function with Genetic Programming
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LinQiu.html
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http___dx.doi.org_10.1109_ICNC.2008.118 http://dx.doi.org/10.1109/ICNC.2008.118
chen:2023:BIC-TA Transformer Surrogate Genetic Programming for Dynamic Container Port Truck Dispatching
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RuibinBai.html
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Chen:2018:ICSESS TEA-MAC: Traffic Estimation Adaptive MAC Protocol for Underwater Acoustic Networks
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GuolanLin.html
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Chen:2007:cec Genetic Network Programming with Sarsa Learning and Its Application to Creating Stock Trading Rules
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JingluHu.html
http___dx.doi.org_10.1109_CEC.2007.4424475 http://dx.doi.org/10.1109/CEC.2007.4424475
Chen2:2008:cec Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices
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KaoruShimada.html
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Chen:2008:gecco Construction of portfolio optimization system using genetic network programming with control nodes
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KaoruShimada.html
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http___dx.doi.org_10.1145_1389095.1389413 http://dx.doi.org/10.1145/1389095.1389413
Chen2:2009:cec Constructing Portfolio Investment Strategy Based on Time Adapting Genetic Network Programming
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EtsushiOhkawa.html
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Chen:2009:ieeeSMC A portfolio selection model using genetic relation algorithm and genetic network programming
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Chen200910735 A portfolio optimization model using Genetic Network Programming with control nodes
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KaoruShimada.html
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Chen200912537 A genetic network programming with learning approach for enhanced stock trading model
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KotaroHirasawa.html
http___dx.doi.org_10.1109_CEC.2010.5586430 http://dx.doi.org/10.1109/CEC.2010.5586430
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KangshunLi.html
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journals/soco/ChenLCW17 Restricted gene expression programming: a new approach for parameter identification inverse problems of partial differential equation
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ZhangxingChen.html
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MingkuiTan.html
http___dx.doi.org_10.1007_978-3-319-93698-7_9 http://dx.doi.org/10.1007/978-3-319-93698-7_9
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journals/tjs/ChenCKHX13 Solving symbolic regression problems with uniform design-aided gene expression programming
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Yuxin_Chen:thesis A Novel Hybrid Focused Crawling Algorithm to Build Domain-Specific Collections
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Chen:2007:WISP A Genetic Programming Approach for Classification of Textures Based on Wavelet Analysis
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Chen:2020:CEC A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching
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Xinan_Chen:ieeeTEC Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching
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Chen:TEVC Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks
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Chen:2020:ICNC Energy Efficient NFV Resource Allocation in Edge Computing Environment
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CHEN:2023:jmst Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning
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Chen:2022:SCC Automatically Design Heuristics for Multi-Objective Location-Aware Service Brokering in Multi-Cloud
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Chen:2023:ITSC Neural Network Assisted Genetic Programming in Dynamic Container Port Truck Dispatching
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Chen:2023:SMC Heuristic Navigation Model Based on Genetic Programming for Multi-UAV Power Inspection Problem with Charging Stations
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Chien:2002:KES A Classifier with the Function-based Decision Tree
Been-ChianChien.html
MickJung-YiLin.html
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Chien:2004:PR Learning effective classifiers with Z-value measure based on genetic programming
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Chien:2006:ICSMC Features Selection based on Rough Membership and Genetic Programming
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Tzung-PeiHong.html
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Chimisliu:2012:AST Category Partition Method and Satisfiability Modulo Theories for test case generation
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FranzWotawa.html
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BarretChin.html
MengjieZhang.html
http___www.mcs.vuw.ac.nz_comp_Publications_archive_CS-TR-07_CS-TR-07-3.pdf http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-07/CS-TR-07-3.pdf
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BarretChin.html
MengjieZhang.html
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VLRajuChinthalapati.html
http___www.lse.ac.uk_Mathematics_Research-Students_PhD-Roll-of-Honour http://www.lse.ac.uk/Mathematics/Research-Students/PhD-Roll-of-Honour
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1144138 Genetic programming for agricultural purposes
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LuisEDaCosta.html
Jacques-AndreLandry.html
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Chion:2008:ieeeTGRS A Genetic-Programming-Based Method for Hyperspectral Data Information Extraction: Agricultural Applications
ClementChion.html
Jacques-AndreLandry.html
LuisEDaCosta.html
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Chittilappilly:2023:ACCAI A Comparative Analysis of Optimizing Medical Insurance Prediction Using Genetic Algorithm and Other Machine Learning Algorithms
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SanjanaSuresh.html
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1277274 A data parallel approach to genetic programming using programmable graphics hardware
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DarrenMChitty.html
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Chitty:2016:SGAI Experiments with High Performance Genetic Programming for Classification Problems
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chitty2017faster Faster GPU-based genetic programming using a two-dimensional stack
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chitty:2018:ukci Exploiting Tournament Selection for Efficient Parallel Genetic Programming
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chitty:2023:GECCOcomp Phased Genetic Programming for Application to the Traveling Salesman Problem
DarrenMChitty.html
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chitty:2023:UKCI Strategies to Apply Genetic Programming Directly to the Traveling Salesman Problem
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chitty:2024:GECCOcomp Greedy Strategies to Improve Phased Genetic Programming When Applied Directly to the Traveling Salesman Problem
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chitty:2024:GECCOcomp2 Improving the Efficiency Of Genetic Programming for Classification Tasks Using a Phased Approach
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Chiu:2001:AGP The Application of Genetic Programming in Milk Yield Prediction for Dairy Cows
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Chivilikhin:2013:GECCO MuACOsm: a new mutation-based ant colony optimization algorithm for learning finite-state machines
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Chivilikhin:2015:GECCOcomp Inferring Temporal Properties of Finite-State Machine Models with Genetic Programming
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cho:1996:mNNeGP Modular Neural Networks Evolved by Genetic Programming
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Choi:2018:SSBSE Learning Fault Localisation for Both Humans and Machines using Multi-Objective GP
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choi:pao:gecco2004 Polynomial Approximation of Survival Probabilities Under Multi-point Crossover
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CHOI:2017:SEC Artificial life based on boids model and evolutionary chaotic neural networks for creating artworks
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Choi:2010:ICIP Computer-aided detection of pulmonary nodules using genetic programming
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Chong:2013:SMC Collaborative Analytics with Genetic Programming for Workflow Recommendation
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Chong:2022:AP-S Using Genetic Programming to Achieve High Broadband Absorptivity Metamaterial in Compact Radar Band (1-11 GHz) without Lossy Materials
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Chong-cun:2018:ICCCAS Hardware Evolution Platform Reasearch Based on Matrix Coding CGP
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choo:2000:EDLBC Emergence of a Division of Labor in a Bee Colony
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chopard2000 Parallel and distributed evolutionary computation for financial applications
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BONDAN-CHORISMA-120155201062 Implementesi Algoritma Grammatical Evolution Menggunakan Steady State Untuk Prediksi Ketinggian Gelombang Laut
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Chou:1998:GC Channel assignment using genetic programming in wireless networks
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Chou:2014:ieeeSMC A dynamic stock trading system based on a Multi-objective Quantum-Inspired Tabu Search algorithm
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chouza09:_passiv_analog_filter_desig_using Passive Analog Filter Design Using GP Population Control Strategies
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Christmas:2015:GECCOcomp Genetic C Programming with Probabilistic Evaluation
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christodoulaki:2023:GECCO Enhanced Strongly Typed Genetic Programming for Algorithmic Trading
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Chu:2016:PPSN Tournament Selection based on Statistical Test in Genetic Programming
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Chu:2017:APSIES Reducing code bloat in Genetic Programming based on subtree substituting technique
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Chuengsatiansup:2022:GI Opportunities for Genetic Improvement of Cryptographic Code
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Ciesielski:1999:AJ Developing a team of soccer playing robots by genetic programming
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ciesielski:2004:ewefigp Experiments with Explicit For-loops in Genetic Programming
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eurogp:CiesielskiIJM05 Understanding Evolved Genetic Programs for a Real World Object Detection Problem
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eurogp07:ciesielski Data Mining of Genetic Programming Run Logs
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Ciesielski:2008:GPEM Linear genetic programming, Springer Science+Business Media, Markus Brameier and Wolfgang Banzhaf, 2007, 315 pp, Book Series: Genetic Programming, Hard Cover, 62.95, ISBN 0-387-31029-0
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Ciftci:2009:EAEI Genetic programming approach to predict a model acidolysis sy