Changes to Genetic Programing Bibliography since 2017/05/20

New and modified entries

New

  1. Abdelmutalab:2016:PC Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers AmeenAbdelmutalab.html KhaledAssaleh.html MohamedEl-Tarhuni.html
  2. Afshar:2017:RSE The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products MehdiHesamiAfshar.html MustafaTolgaYilmaz.html
  3. AlMosawe:2017:CS Strength of Cfrp-steel double strap joints under impact loads using genetic programming AlaaAl-Mosawe.html RobinKalfat.html RiadhAl-Mahaidi.html
  4. Ammar:2016:Neurocomputing Multi-agent architecture for Multiaobjective optimization of Flexible Neural Tree MarwaAmmar.html SouhirBouaziz.html AdelMAlimi.html AjithAbraham.html
  5. Anicic:2017:OLE Prediction of laser cutting heat affected zone by extreme learning machine ObradAnicic.html SrdanJovic.html HivzoSkrijelj.html BogdanNedic.html
  6. Arsalan:2017:ASC Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique MuhammadArsalanAwan.html AqsaSaeedQureshi.html AsifullahKhan.html MuttukrishnanRajarajan.html
  7. Babanajad:2017:AES New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach SaeedKBabanajad.html AHGandomi.html AHAlavi.html
  8. Baker:2014:DBV:2638404.2638521 Detecting Bacterial Vaginosis Using Machine Learning YolandaSBaker.html RajeevAgrawal.html JamesAFoster.html DanielBeck.html GerryDozier.html
  9. Baker:2014:ICMLC Applying machine learning techniques in detecting Bacterial Vaginosis YolandaSBaker.html RajeevAgrawal.html JamesAFoster.html DanielBeck.html GerryDozier.html
  10. Baral:2017:SSI Impedance spectroscopy of Gd-doped ceria analyzed by genetic programming (ISGP) method BaralAshok-Kumar.html YoedTsur.html
  11. Bardool:2016:JML Phase stability conditions of clathrate hydrates for methane + aqueous solution of water soluble organic promoter system: Modeling using a thermodynamic framework RoghayehBardool.html JafarJavanmardi.html AliakbarRoosta.html AmirHMohammadi.html
  12. Baser:2017:Energy A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation FurkanBaser.html HaydarDemirhan.html
  13. Beck:2015:BDM Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis DanielBeck.html JamesAFoster.html
  14. bruce:RN1701 Approximate Oracles and Synergy in Software Energy Search Spaces BobbyRBruce.html JustynaPetke.html MarkHarman.html EarlBarr.html
  15. Cao:2016:EM Spatially-explicit forecasting of cyanobacteria assemblages in freshwater lakes by multi-objective hybrid evolutionary algorithms Hong-QingCao.html FriedrichRecknagel.html MichaelBartkow.html
  16. castelli:2017:jaihc Predicting per capita violent crimes in urban areas: an artificial intelligence approach MauroCastelli.html RaulSormani.html LeonardoTrujillo.html AlesPopovic.html
  17. Castelli:2017:SEC The influence of population size in geometric semantic GP MauroCastelli.html LucaManzoni.html SaraSilva.html LeonardoVanneschi.html AlesPopovic.html
  18. 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
  19. Castelli:2017:CandC An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming MauroCastelli.html LeonardoTrujillo.html IvoGoncalves.html AlesPopovic.html
  20. Cramer:2017:ESA An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives SamCramer.html MichaelKampouridis.html AlexAlvesFreitas.html AntonisKAlexandridis.html
  21. DanandehMehr:2017:JH A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction AliDanandehMehr.html ErcanKahya.html
  22. Derouich:2017:NA General model of depolarization and transfer of polarization of singly ionized atoms by collisions with hydrogen atoms MoncefDerouich.html
  23. Dindarloo:2016:JSR Off-road truck-related accidents in U.S. mines SaeidRDindarloo.html JonishaPPollard.html ElnazSiami-Irdemoosa.html
  24. Downing08PhD Artificial evolution with Binary Decision Diagrams: a study in evolvability in neutral spaces RichardMarkDowning.html
  25. Durasevic:2016:ASC Adaptive scheduling on unrelated machines with genetic programming MarkoDurasevic.html DomagojJakobovic.html KarloKnezevic.html
  26. Elola:2017:ASC Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems AndoniElola.html JavierDelSer.html MirenNekaneBilbao.html CristinaPerfecto.html EnriqueAlexandre.html SanchoSalcedo-Sanz.html
  27. EnriquezZarate:2017:ASC Automatic modeling of a gas turbine using genetic programming: An experimental study JosueEnriquez-Zarate.html LeonardoTrujillo.html SalvadordeLara.html MauroCastelli.html EmigdioZ-Flores.html LuisMunozDelgado.html AlesPopovic.html
  28. Fanjiang:2016:IST Search based approach to forecasting QoS attributes of web services using genetic programming Yong-YiFanjiang.html YangSyu.html Jong-YihKuo.html
  29. Fenton:2014:AC Automatic innovative truss design using grammatical evolution MichaelFenton.html CiaranMcNally.html JonathanByrne.html ErikHemberg.html JamesMcDermott.html MichaelO'Neill.html
  30. FernandezAres:2017:EC Analysing the influence of the fitness function on genetically programmed bots for a real-time strategy game AntonioFernandez-Ares.html AntonioMMoraGarcia.html PabloGarcia-Sanchez.html PedroACastilloValdivieso.html JuanJulianMerelo.html
  31. Folino:2016:ASC Evolving meta-ensemble of classifiers for handling incomplete and unbalanced datasets in the cyber security domain GianluigiFolino.html FrancescoSergioPisani.html
  32. Freitag:2016:AMT Automatic design of scheduling rules for complex manufacturing systems by multi-objective simulation-based optimization MichaelFreitag.html TorstenHildebrandt.html
  33. Gandomi:2017:Measurement Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement AHGandomi.html AHAlavi.html MostafaGandomi.html SadeghKazemi.html
  34. Garg:2015:Measurement Measurement of environmental aspect of 3-D printing process using soft computing methods AkhilGarg.html JasmineSiuLeeLam.html
  35. Garg:2015:Measurementa Model development based on evolutionary framework for condition monitoring of a lathe machine AkhilGarg.html VenkateshVijayaraghavan.html KangTai.html PravinMSingru.html VishalJain.html NikileshKrishnakumar.html
  36. Garg:2016:JCP Power consumption and tool life models for the production process AkhilGarg.html JasmineSiuLeeLam.html
  37. Garg:2016:JCPb Study of effect of nanofluid concentration on response characteristics of machining process for cleaner production AkhilGarg.html ShrutidharaSarma.html BiranchiNarayanPanda.html JianZhang.html LiangGao.html
  38. Garg:2017:ASC A hybrid computational intelligence framework in modelling of coal-oil agglomeration phenomenon AkhilGarg.html JasmineSiuLeeLam.html BiranchiNarayanPanda.html
  39. Ghanbari:2017:Fuel Performance and emission characteristics of a CI engine using nano particles additives in biodiesel-diesel blends and modeling with GP approach MGhanbari.html GholamHasanNajafi.html BaratGhobadian.html TalalYusaf.html AntonioPaoloCarlucci.html MostafaKianiDehKiani.html
  40. Harding:2016:DS Meta-Parametric Design JohnEHarding.html PaulShepherd.html
  41. HosseiniMonazzah:2017:CI Influence of interfacial adhesion on the damage tolerance of Al6061/SiCp laminated composites AsalHosseiniMonazzah.html HesamPouraliakbar.html MohammadRezaJandaghi.html RezaBagheri.html SeyedMortezaSeyedReihani.html
  42. Jamiolahmadi:2016:Measurement Study of detailed deviation zone considering coordinate metrology uncertainty SaeedJamiolahmadi.html AhmadBarari.html
  43. Jiang:2016:CILS Model development and surface analysis of a bio-chemical process DazhiJiang.html Wan-HuanZhou.html AnkitGarg.html AkhilGarg.html
  44. Kamali:2017:AMM Takagi-Sugeno fuzzy modelling of some nonlinear problems using ant colony programming MohdZahurinBinMohamedKamali.html NallasamyKumaresan.html KuruRatnavelu.html
  45. Kazaryan:2017:PCS Grammatical Evolution for Neural Network Optimization in the Control System Synthesis Problem DavidKazaryan.html AVSavinkov.html
  46. Kazemi:2016:PT Computational intelligence modeling of granule size distribution for oscillating milling PezhmanKazemi.html MohammadHassanKhalid.html JakubSzlek.html AndrejaMirtic.html GavinKReynolds.html RenataJachowicz.html AleksanderMendyk.html
  47. Klusacek:2016:IFAC-PapersOnLine Comparing Fitness Functions for Genetic Feature Transformation JanKlusacek.html VaclavJirsik.html
  48. Kulkarni20112752 Colon cancer prediction with genetics profiles using evolutionary techniques AshwinikumarKulkarni.html BSCNaveenKumar.html VadlamaniRavi.html UpadhyayulaSuryanarayanaNMurthy.html
  49. Kulunchakov:2017:ESA Generation of simple structured Information Retrieval functions by genetic algorithm without stagnation AndreyKulunchakov.html VadimStrijov.html
  50. Liang:2017:ASC Genetic programming for evolving figure-ground segmentors from multiple features YuyuLiang.html MengjieZhang.html WillNBrowne.html
  51. Liang:2017:EAAI Image feature selection using genetic programming for figure-ground segmentation YuyuLiang.html MengjieZhang.html WillNBrowne.html
  52. Lin:2017:Vacuum Microstructural evolution and constitutive models to predict hot deformation behaviors of a nickel-based superalloy YCLin.html Fu-QiNong.html Xiao-MinChen.html Dong-DongChen.html Ming-SongChen.html
  53. MahmoodAlJuboori:2016:JH A stepwise model to predict monthly streamflow AnasMahmoodAl-Juboori.html AytacGuven.html
  54. Manahov:2016:IRFA A note on the relationship between high-frequency trading and latency arbitrage ViktorManahov.html
  55. Marjanovic:2016:JU Prediction of GDP growth rate based on carbon dioxide (CO2) emissions VladislavMarjanovic.html MilosMilovancevic.html IgorMladenovic.html
  56. Markovic:2017:PASMA Soft computing prediction of economic growth based in science and technology factors DusanMarkovic.html DaliborPetkovic.html VlastimirNikolic.html MilosMilovancevic.html BiljanaPetkovic.html
  57. MasoumiShahrBabak:2016:AOR Uplift capacity prediction of suction caisson in clay using a hybrid intelligence method (GMDH-HS) MojtabaMasoumiShahr-Babak.html MohammadJavadKhanjani.html KouroshQaderi.html
  58. Mladenovic:2016:RSER Management and estimation of thermal comfort, carbon dioxide emission and economic growth by support vector machine IgorMladenovic.html SvetlanaSokolov-Mladenovic.html MilosMilovancevic.html DusanMarkovic.html NenadSimeunovic.html
  59. MohamadiBaghmolaei:2016:JML Novel Method for estimation of Gas/Oil relative Permeabilities MohamadMohamadi-Baghmolaei.html RezaAzin.html ZahraSakhaei.html RezvanMohamadi-Baghmolaei.html ShahriarOsfouri.html
  60. MohamadiBaghmolaei:2016:JMLa Novel method for estimation of gas/oil relative permeabilities MohamadMohamadi-Baghmolaei.html RezaAzin.html ZahraSakhaei.html RezvanMohamadi-Baghmolaei.html ShahriarOsfouri.html
  61. Mojumder:2017:RSER The intelligent forecasting of the performances in PV/T collectors based on soft computing method JuwelChandraMojumder.html HwaiChyuanOng.html WenTongChong.html NimaIzadyar.html ShahaboddinShamshirband.html
  62. Ouyang:2017:JCH Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method QiOuyang.html WenxiLu.html ZeyuHou.html YuZhang.html ShuaiLi.html JiannanLuo.html
  63. Oz:2017:JPS A novel approach for supercapacitors degradation characterization AlonOz.html DannyGelman.html EmanuelleGoren.html NetaShomrat.html SiomaBaltianski.html YoedTsur.html
  64. Parque:2017:Neurocomputing A method to learn high-performing and novel product layouts and its application to vehicle design VictorParque.html TomoyukiMiyashita.html
  65. Parsaie:2017:FMI Prediction of flow discharge in compound open channels using adaptive neuro fuzzy inference system method AbbasParsaie.html HojjatallahYonesi.html ShadiNajafian.html
  66. PatilShinde:2016:JECE The Removal of arsenite [As(III)] and arsenate [As(V)] ions from wastewater using TFA and TAFA resins: Computational intelligence based reaction modeling and optimization VeenaPatil-Shinde.html KBMulani.html KaminiDonde.html NNChavan.html SPonrathnam.html SanjeevSTambe.html
  67. Petkovic:2016:Mechatronics Analyzing of flexible gripper by computational intelligence approach DaliborPetkovic.html SrdanJovic.html ObradAnicic.html BogdanNedic.html BrankoPejovic.html
  68. Picek:2017:DSD:3075564.3079069 Design of S-boxes Defined with Cellular Automata Rules StjepanPicek.html LucaMariot.html BohanYang.html DomagojJakobovic.html NeleMentens.html
  69. Picek:2017:GECCO Evolving S-boxes Based on Cellular Automata with Genetic Programming StjepanPicek.html LucaMariot.html AlbertoLeporati.html DomagojJakobovic.html
  70. Pourzangbar:2017:CE Prediction of non-breaking wave induced scour depth at the trunk section of breakwaters using Genetic Programming and Artificial Neural Networks AliPourzangbar.html MiguelALosada.html AnisehSaber.html LidaRasoulAhari.html PhilippeLarroude.html MostafaVaezi.html MaurizioBrocchini.html
  71. Rathore:2017:ESA Towards an ensemble based system for predicting the number of software faults SantoshSRathore.html SandeepKumar.html
  72. Salem:2017:ASC Classification of human cancer diseases by gene expression profiles HanaaSalem.html GamalMahrousAliAttiya.html NawalAhmedEl-Fishawy.html
  73. Seesing:mastersthesis EvoTest Test Case Generation Using Genetic Programming and Software Analysis ArjanSeesing.html
  74. Sipper:2017:arxiv From MEGATON to RASCAL: Surfing the Parameter Space of Evolutionary Algorithms MosheSipper.html WeixuanFu.html KarunaAhuja.html JasonHMoore.html
  75. Sohani:2016:ATE A novel approach using predictive models for performance analysis of desiccant enhanced evaporative cooling systems AliSohani.html HoseynSayyaadi.html HamidrezaHasaniBalyani.html SinaHoseinpoori.html
  76. Sohani:2017:ATE A comprehensive performance investigation of cellulose evaporative cooling pad systems using predictive approaches AliSohani.html MitraZabihigivi.html MohammadHosseinMoradi.html HoseynSayyaadi.html HamidrezaHasaniBalyani.html
  77. Vijayaraghavan:2017:Measurement Thermo-mechanical modeling of metallic alloys for nuclear engineering applications VenkateshVijayaraghavan.html AkhilGarg.html KangTai.html LiangGao.html
  78. Wang:2017:JH Physically sound formula for longitudinal dispersion coefficients of natural rivers Yu-FeiWang.html Wen-XinHuai.html Wei-JieWang.html
  79. Xue:GPEM:bookreview Sebastian Ventura and Jose Maria Luna: Pattern mining with evolutionary algorithms BingXue.html
  80. Yadav:2016:Measurement Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany BasantYadav.html SudheerCh.html ShashiMathur.html JanAdamowski.html
  81. yin:2016:jaiscr An Analysis of the Performance of Genetic Programming for Realised Volatility Forecasting ZhengYin.html ConallO'Sullivan.html AnthonyBrabazon.html
  82. Yoo:2015:SSBSE Amortised Optimisation of Non-functional Properties in Production Environments ShinYoo.html
  83. Yoo:TOSEM:sbfl Human Competitiveness of Genetic Programming in Spectrum Based Fault Localisation: Theoretical and Empirical Analysis ShinYoo.html XiaoYuanXie.html Fei-Ching_Diana_Kuo.html TsongYuehChen.html MarkHarman.html
  84. Zhong:2017:ASC Automatic model construction for the behavior of human crowds JinghuiZhong.html WentongCai.html MichaelLees.html LinboLuo.html
  85. Zhou:2016:Measurement Study of the volumetric water content based on density, suction and initial water content Wan-HuanZhou.html AnkitGarg.html AkhilGarg.html

Modified

  1. 5 Agapitos:2016:GPEM Recursion in tree-based genetic programming AlexandrosAgapitos.html MichaelO'Neill.html AhmedKattan.html SimonMLucas.html
  2. 6 Alavi:2016:GSF Progress of machine learning in geosciences: Preface AHAlavi.html AHGandomi.html DavidJohnLary.html
  3. 1 Alexander:2016:PPSN Using Scaffolding with Partial Call-Trees to Improve Search BradAlexander.html ConniePyromallis.html GeorgeLorenzetti.html BradZacher.html
  4. 11 angeline:1996:leaf An Investigation into the Sensitivity of Genetic Programming to the Frequency of Leaf Selection During Subtree Crossover PeterJohnAngeline.html
  5. 2 Armani_2010 Enhancements to a hybrid genetic programming technique applied to symbolic regression UmbertoArmani.html VassiliVToropov.html AndreyPolynkin.html OsvaldoMQuerin.html LuisFAlvarez.html
  6. 1 Azad:2017:GPEM Krzysztof Krawiec: Behavioral program synthesis with genetic programming RMuhammadAtifAzad.html
  7. 1 banzhaf:2000:IJ Hierarchical Genetic Programming using Local Modules WolfgangBanzhaf.html DirkBanscherus.html PeterDittrich.html
  8. 3 Beck:2014:PLoSONE Machine Learning Techniques Accurately Classify Microbial Communities by Bacterial Vaginosis Characteristics DanielBeck.html JamesAFoster.html
  9. 24 Bokhari:2017:GI Deep Parameter Optimisation on Android Smartphones for Energy Minimisation - A Tale of Woe and a Proof-of-Concept MahmoudABokhari.html BobbyRBruce.html BradAlexander.html MarkusWagner.html
  10. 6 Boukhelifa:2016:EC Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search NadiaBoukhelifa.html AnastasiaBezerianos.html WaldoCancino.html EvelyneLutton.html
  11. 2 Bucur:2014:ASC The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation DoinaBucur.html GiovanniIacca.html GiovanniSquillero.html AlbertoTonda.html
  12. 1 Burks:2016:GPEM An analysis of the genetic marker diversity algorithm for genetic programming ArmandRBurks.html WilliamFPunch.html
  13. 5 Cano:2016:SC Multi-objective genetic programming for feature extraction and data visualization AlbertoCanoRojas.html SebastianVentura.html KrzysztofJCios.html
  14. 2 Castelli:2016:CIN Controlling Individuals Growth in Semantic Genetic Programming through Elitist Replacement MauroCastelli.html LeonardoVanneschi.html AlesPopovic.html
  15. 1 Chaumont:2016:GPEM Evolution of sustained foraging in three-dimensional environments with physics NicolasChaumont.html ChristophAdami.html
  16. 5 Chen:2016:JPDC enDebug: A hardware-software framework for automated energy debugging JieChen.html GuruPrasadhVenkataramani.html
  17. 1 Chen:2001:ICCIMA1 Evolving Bargaining Strategies with Genetic Programming: An Overview of AIE-DA, Ver. 2, Part 1 Shu-HengChen.html
  18. 1 Chen:2001:ICCIMA2 Evolving Bargaining Strategies with Genetic Programming: An Overview of AIE-DA, Ver. 2, Part 2 Shu-HengChen.html Bin-TzongChie.html Chung-ChingTai.html
  19. 22 Cody-Kenny:2017:GI From Problem Landscapes to Language Landscapes: Questions in Genetic Improvement BrendanCody-Kenny.html MichaelFenton.html MichaelO'Neill.html
  20. 1 Cramer:2017:evoApplications Pricing Rainfall Based Futures Using Genetic Programming SamCramer.html MichaelKampouridis.html AlexAlvesFreitas.html AntonisKAlexandridis.html
  21. 2 Dempster:2000:wp35 The Profitability of Intra-Day FX Trading Using Technical Indicators MichaelDempster.html ChrisMJones.html
  22. 4 Diaz-Alvarez:2016:SC Optimizing L1 cache for embedded systems through grammatical evolution JosefaDiazAlvarez.html JManuelColmenar.html JoseLRisco-Martin.html JLanchares.html OscarGarnica.html
  23. 2 Diveev:2014:MED Symbolic regression methods for control system synthesis AskatDiveev.html DavidKazaryan.html ElenaASofronova.html
  24. 4 Dolado:2016:ASOC Evaluation of Estimation Models using the Minimum Interval of Equivalence JoseJavierDoladoCosin.html DanielRodriguez.html MarkHarman.html WilliamBLangdon.html FedericaSarro.html
  25. 1 Ekart:2017:GPEM Genotype-phenotype mapping implications for genetic programming representation: Commentary on ``On the mapping of genotype to phenotype in evolutionary algorithms'' by Peter A. Whigham, Grant Dick, and James Maclaurin AnikoEkart.html PeterRLewis.html
  26. 13 Ekart:2017:GI Gaining Insights into Traffic Data through Genetic Improvement AnikoEkart.html AlinaPatelli.html VictoriaLush.html ElisabethIlie-Zudor.html
  27. 1 Elhenawy:2014:TRPCET Dynamic travel time prediction using data clustering and genetic programming MohammedElhenawy.html HaoChen2.html HeshamARakha.html
  28. 35 Elver:2016:ieeeHPCA McVerSi: A Test Generation Framework for Fast Memory Consistency Verification in Simulation MarcoElver.html VijayNagarajan.html
  29. 6 Estebanez:2014:CI Automatic Design of Noncryptographic Hash Functions using Genetic Programming CesarEstebanez.html YagoSaez.html GustavoRecioIsasi.html PedroIsasiVinuela.html
  30. 1 Fattah:2016:JKSUES Improved oil formation volume factor (Bo) correlation for volatile oil reservoirs: An integrated non-linear regression and genetic programming approach KhaledAbdelFattahElshreef.html ALashin.html
  31. 5 Fenton:2016:ieeeTEC Discrete Planar Truss Optimization by Node Position Variation using Grammatical Evolution MichaelFenton.html CiaranMcNally.html JonathanByrne.html ErikHemberg.html JamesMcDermott.html MichaelO'Neill.html
  32. 5 Fu:2016:SC Genetic programming for edge detection: a Gaussian-based approach WenlongFu.html MarkJohnston.html MengjieZhang.html
  33. 1 Gandomi:2011:Discussion Discussion: Neural Network -- Genetic Programming for Sediment Transport AHGandomi.html
  34. 3 Garg:2016:JCPa Modeling multiple-response environmental and manufacturing characteristics of EDM process AkhilGarg.html JasmineSiuLeeLam.html LiangGao.html
  35. 6 Ghugare:2016:JEI Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies SuhasBGhugare.html SanjeevSTambe.html
  36. 32 conf/epia/GoncalvesSF15 Semantic Learning Machine: A Feedforward Neural Network Construction Algorithm Inspired by Geometric Semantic Genetic Programming IvoGoncalves.html SaraSilva.html CarlosMFonseca.html
  37. 26 Haraldsson:2017:SBST The Use of Automatic Test Data Generation for Genetic Improvement in a Live System SaemundurOskarHaraldsson.html JohnRWoodward.html AlexanderEIBrownlee.html
  38. 24 Haraldsson:2017:GI Fixing Bugs in Your Sleep: How Genetic Improvement Became an Overnight Success SaemundurOskarHaraldsson.html JohnRWoodward.html AlexanderEIBrownlee.html KristinSiggeirsdottir.html
  39. 25 Haraldsson:2017a:GI Genetic Improvement of Runtime in a Bioinformatics Application SaemundurOskarHaraldsson.html JohnRWoodward.html AlexanderEIBrownlee.html AlbertVSmith.html VilmundurGudnason.html
  40. 5 He:2015:SC Model approach to grammatical evolution: theory and case study PeiHe.html ZelinDeng.html HouFengWang.html ZhusongLiu.html
  41. 5 Javed:2015:MTA Multi-Denoising based Impulse Noise Removal from Images using Robust Statistical Features and Genetic Programming SyedGibranJaved.html AbdulMajid.html AnwarMMirza.html AsifullahKhan.html
  42. 7 Kiani:2016:jmce New Formulation of Compressive Strength of Preformed-Foam Cellular Concrete: An Evolutionary Approach BehnamKiani.html AHGandomi.html SiavashSajedi.html RobertYLiang.html
  43. 2 PolyfinicJAR Genetic Programming + Proof Search = Automatic Improvement ZoltanKocsis.html JerrySwan.html
  44. 1 Kovacic:2016:RMZ Natural gas consumption prediction in Slovenian industry - a case study MihaKovacic.html BozidarSarler.html UrosZuperl.html
  45. 1 Kovacic:2017:KomPlasTech Usage of evolutionary methods in Store Steel Ltd. steel plant DanutaSzeliga.html LukaszRauch.html
  46. 1 Kriegman:2016:PPSN Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling SamKriegman.html MarcinSzubert.html JoshCBongard.html ChristianSkalka.html
  47. 37 Landsborough:2017:GI Learning from Super-Mutants JasonLandsborough.html StephenHarding.html SunnyFugate.html
  48. 1 Langdon:2017:GI Improving SSE Parallel Code with Grow and Graft Genetic Programming WilliamBLangdon.html RonnyLorenz.html
  49. 6 Lary:2016:GSF Machine learning in geosciences and remote sensing DavidJohnLary.html AHAlavi.html AHGandomi.html AnnetteLWalker.html
  50. 5 Liu:2015:Cybernetics Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach LiLiu.html LingShao.html XuelongLi.html KeLu.html
  51. 1 Liu:2014:IS Evolutionary compact embedding for large-scale image classification LiLiu.html LingShao.html XuelongLi.html
  52. 1 Lopez-Lopez:2016:CyS Comparison of Local Feature Extraction Paradigms Applied to Visual SLAM VictorRaulLopezLopez.html LeonardoTrujillo.html PierrickLegrand.html VictorHDiaz-Ramirez.html GustavoOlague.html
  53. 5 Mariani:2017:IST A systematic review on search-based refactoring ThainaMariani.html SilviaReginaVergilio.html
  54. 22 PeterMartin:masters An Investigation into the use of Genetic Programming for Intelligent Network Service Creation PeterMartin.html
  55. 4 martin:2000:GPscin Genetic Programming for Service Creation in Intelligent Networks PeterMartin.html
  56. 1 martin:2001:gecco Building a Taxonomy of Genetic Programming PeterMartin.html
  57. 1 oai:CiteSeerPSU:566603 Analysis of the Behavior of a Hardware Implementation of GP using FPGAs and Handel-C PeterMartin.html RiccardoPoli.html
  58. 1 oai:CiteSeerPSU:569263 An Analysis of Random Number Generators for a Hardware Implementation of Genetic Programming using FPGAs and Handel-C PeterMartin.html
  59. 1 martin:2002:EuroGP A Pipelined Hardware Implementation of Genetic Programming using FPGAs and Handel-C PeterMartin.html
  60. 11 martin2:2002:gecco Crossover Operators For a Hardware Implementation Of GP Using FPGAs and Handel-C PeterMartin.html RiccardoPoli.html
  61. 12 martin:2002:gecco An Analysis Of Random Number Generators For a Hardware Implementation of Genetic Programming Using FPGAs And Handel-C PeterMartin.html
  62. 1 PNAS-2009-Menon-16829-34 A state-mutating genetic algorithm to design ion-channel models VilasMenon.html NelsonSpruston.html WilliamLKath.html
  63. 26 Mirzahosseini:2010:ISAP Evaluation of Rutting Potential of Asphalt Mixtures Using Linear Genetic Programming MohammadRezaMirzahosseini.html AHAlavi.html FereidoonMoghaddasNejad.html AHGandomi.html MahmoudAmeri.html
  64. 1 Ni:2016:BioMed Learning from Life-Logging Data by Hybrid HMM: A Case Study on Active States Prediction JiNi.html TryphonLambrou.html XujiongYe.html
  65. 1 nikolaev:2001:TEC Regularization Approach to Inductive Genetic Programming NikolayNikolaev.html HitoshiIba.html
  66. 1 Nobile:thesis Evolutionary Inference of Biological Systems Accelerated on Graphics Processing Units MarcoNobile.html
  67. 1 Olson2016EvoBio Automating Biomedical Data Science Through Tree-Based Pipeline Optimization RandalSOlson.html RyanJUrbanowicz.html PeterCAndrews.html NicoleALavender.html LaCreisReneeKidd.html JasonHMoore.html
  68. 16 Orlov:2017:GI Evolving Software Building Blocks with FINCH MichaelOrlov.html
  69. 13 Parada:2013:CEC Automatic Generation of Algorithms for the Binary Knapsack Problem LucasPradenasParada.html MauricioSepulveda.html CarlosHerrera.html VictorParada.html
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