%% Genetic Programming Bibliography %%$Revision: 1.8081 $ $Date: 2024/11/29 19:51:44 $ %%Created by W.B.Langdon cs.ucl.ac.nl January 1995 %%Based on J.Koza's GP bibliography of 14 March 1994 %% To add references to your papers see %% ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/biblio/ %%optional References [AA02] Alexandre P. Alves da Silva and Pedro Jose Abrao. Applications of evolutionary computation in electric power systems. In David B. Fogel, Mohamed A. El-Sharkawi, Xin Yao, Garry Greenwood, Hitoshi Iba, Paul Marrow, and Mark Shackleton, editors, Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, pages 1057--1062. IEEE Press, 12-17 May 2002. [AA04] J. Aguilar and J. Altamiranda. A data mining algorithm based on the genetic programming, 2004. [AA05] Wendy Ashlock and Dan Ashlock. Single parent genetic programming. In David Corne, Zbigniew Michalewicz, Marco Dorigo, Gusz Eiben, David Fogel, Carlos Fonseca, Garrison Greenwood, Tan Kay Chen, Guenther Raidl, Ali Zalzala, Simon Lucas, Ben Paechter, Jennifier Willies, Juan J. Merelo Guervos, Eugene Eberbach, Bob McKay, Alastair Channon, Ashutosh Tiwari, L. Gwenn Volkert, Dan Ashlock, and Marc Schoenauer, editors, Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 2, pages 1172--1179, Edinburgh, UK, 2-5 September 2005. IEEE Press. [AA10] Ala' S. Al-Afeef. Image reconstructing in electrical capacitance tomography of manufacturing processes using genetic programming. Master's thesis, Al-Balqa Applied University, Al-Salt, Jordan, July 2010. [AA11] Wendy Ashlock and Daniel Ashlock. Designing artificial organisms for use in biological simulations. In IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2011), Paris, 11-15 April 2011. [AA12a] H. Md. Azamathulla and Z. Ahmad. Gene-expression programming for transverse mixing coefficient. Journal of Hydrology, 434-435:142--148, April 2012. [AA12b] H. Md. Azamathulla and Z. Ahmad. GP approach for critical submergence of intakes in open channel flows. Journal of Hydroinformatics, 14(4):937--943, October 2012. [AA17a] Hakan Ayral and Songul Albayrak. Effects of population, generation and test case count on grammatical genetic programming for integer lists. Journal of Software, 12(6):483--492, June 2017. [AA17b] Hakan Ayral and Songul Albayrak. Parallel and in-process compilation of individuals for genetic programming on GPU. PeerJ PrePrints, 5:e2936, 2017. [AA21a] Davut Ari and Baris Baykant Alagoz. A genetic programming based pollutant concentration predictor design for urban pollution monitoring based on multi-sensor electronic nose. In 2021 International Conference on Information Technology (ICIT), pages 168--172, July 2021. [AA21b] Davut Ari and Baris Baykant Alagoz. Modeling daily financial market data by using tree-based genetic programming. In 2021 International Conference on Information Technology, ICIT, pages 382--386, Amman, Jordan, 14-15 July 2021. IEEE. [AA22] Davut Ari and Baris Baykant Alagoz. An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application. Neural Computing and Applications, 34(15), 2022. [AA23] Davut Ari and Baris Baykant Alagoz. DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction. Soft Computing, 27(5):2553--2574, March 2023. [AAA08] Ali Aytek, M Asce, and Murat Alp. An application of artificial intelligence for rainfall-runoff modeling. Journal of Earth System Science, 117(2):145--155, April 2008. [AAA+09] M. Arvaneh, H. Ahmadi, A. Azemi, M. Shajiee, and Z. S. Dastgheib. Prediction of paroxysmal atrial fibrillation by dynamic modeling of the PR interval of ECG. In International Conference on Biomedical and Pharmaceutical Engineering, ICBPE '09, pages 1--5, 2-4 December 2009. [AAA13] H. Md. Azamathulla, Zulfequar Ahmad, and Aminuddin Ab. Ghani. An expert system for predicting manning's roughness coefficient in open channels by using gene expression programming. Neural Computing and Applications, 23(5):1343--1349, 2013. [AAA15] Mohammed Alweshah, Walid Ahmed, and Hamza Aldabbas. Evolution of software reliability growth models: A comparison of auto-regression and genetic programming models. International Journal of Computer Applications, 125(3):20--25, September 2015. [AAA+18] K. P. Amber, R. Ahmad, M. W. Aslam, A. Kousar, M. Usman, and M. S. Khan. Intelligent techniques for forecasting electricity consumption of buildings. Energy, 157:886--893, 2018. [AAA+23a] Abdulaziz Alaskar, Ghasan Alfalah, Fadi Althoey, Mohammed Awad Abuhussain, Muhammad Faisal Javed, Ahmed Farouk Deifalla, and Nivin A. Ghamry. Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature. Case Studies in Construction Materials, 18:e02199, 2023. [AAA23b] Mehmet Safa Aydogan, Sema Alacali, and Guray Arslan. Prediction of moment redistribution capacity in reinforced concrete beams using gene expression programming. Structures, 47:2209--2219, 2023. [AAA+24a] Hamdan Alanzi, Hamoud Alenezi, Oladayo Adeyi, Abiola J. Adeyi, Emmanuel Olusola, Chee-Yuen Gan, and Olusegun Abayomi Olalere. Process optimization, multi-gene genetic programming modeling and reliability assessment of bioactive extracts recovery from phyllantus emblica. Journal of Engineering Research, 2024. [AAA+24b] Saad Alatefi, Okorie Ekwe Agwu, Reda Abdel Azim, Ahmad Alkouh, and Iskandar Dzulkarnain. Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure. Chemical Engineering Research and Design, 2024. [AAAD18] Okorie E. Agwu, Julius U. Akpabio, Sunday B. Alabi, and Adewale Dosunmu. Settling velocity of drill cuttings in drilling fluids: A review of experimental, numerical simulations and artificial intelligence studies. Powder Technology, 339:728--746, 2018. [AAB12] Fathi Abid, Wafa Abdelmalek, and Sana Ben Hamida. Dynamic hedging using generated genetic programming implied volatility models. In Sebastian Ventura, editor, Genetic Programming - New Approaches and Successful Applications, chapter 7, pages 141--172. InTech, 2012. [AAB13] Corneliu T. C. Arsene, Denisa Ardevan, and Paul Bulzu. Reverse engineering methodology for bioinformatics based on genetic programming, differential expression analysis and other statistical methods. In Enrico Formenti, Roberto Tagliaferri, and Ernst Wit, editors, CIBB, volume 8452 of Lecture Notes in Computer Science, pages 161--177. Springer, 2013. [AAB+22] Mahmoud Al Najar, Rafael Almar, Erwin W. J. Bergsma, Jean-Marc Delvit, and Dennis G. Wilson. Genetic improvement of shoreline evolution forecasting models. In Bobby R. Bruce, Vesna Nowack, Aymeric Blot, Emily Winter, W. B. Langdon, and Justyna Petke, editors, GI @ GECCO 2022, pages 1916--1923, Boston, USA, 9 July 2022. Association for Computing Machinery. [AAB+23] Mahmoud Al Najar, Rafael Almar, Erwin W. J. Bergsma, Jean-Marc Delvit, and Dennis G. Wilson. Improving a shoreline forecasting model with symbolic regression. In ICLR 2023 Workshop on Tackling Climate Change with Machine Learning, Kigali Rwanda, 4 May 2023. [AABC23] Tasos Asonitis, Richard Allmendinger, Matt Benatan, and Ricardo Climent. SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound. Genetic Programming and Evolvable Machines, 24:article no. 3, 2023. Special Issue: Evolutionary Computation in Art, Music and Design. [AACL99] John A. Atkinson-Abutridy and Julio R. Carrasco-Leon. An evolutionary model for dynamically controlling a behavior-based autonomous agent. In Scott Brave and Annie S. Wu, editors, Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference, pages 16--24, Orlando, Florida, USA, 13 July 1999. [AAd+05] Abdel Latif Abu Dalhoum, Moh'd Al Zoubi, Marina de la Cruz, Alfonso Ortega, and Manuel Alfonseca. A genetic algorithm for solving the p-median problem. In J. Manuel Feliz Teixeira and A. E.Carvalho Brito, editors, European Simulation and Modeling Conference ESM'2005, pages 141--145, Porto, Portugal, October 24-26 2005. http://www.eurosis.org. [AAD11] J. Altamiranda, J. Aguilar, and C. Delamarche. Similarity of amyloid protein motif using an hybrid intelligent system. IEEE Latin America Transactions (Revista IEEE America Latina), 9(5):700--710, September 2011. In Spanish. [AAD13] Junior Altamiranda, Jose Aguilar, and Chistian Delamarche. Comparison and fusion model in protein motifs. In XXXIX Latin American Computing Conference (CLEI 2013), Naiguata, 7-11 October 2013. IEEE. [AAD20a] Mohamed Aliwi, Selcuk Aslan, and Sercan Demirci. Firefly programming for symbolic regression problems. In 2020 28th Signal Processing and Communications Applications Conference (SIU), October 2020. [AAD20b] Dario Alviso, Guillermo Artana, and Thomas Duriez. Prediction of biodiesel physico-chemical properties from its fatty acid composition using genetic programming. Fuel, 264:116844, 2020. [AAd20c] Felipe S. P. Andrade, Claus Aranha, and Ricardo da Silva Torres. On the use of predation to shape evolutionary computation. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pages 117--124, December 2020. [AAD21] Okorie Ekwe Agwu, Julius Udoh Akpabio, and Adewale Dosunmu. Modeling the downhole density of drilling muds using multigene genetic programming. Upstream Oil and Gas Technology, 6:100030, 2021. [AAE18] Mohamed Abdelwhab, A. A. Abouelsoud, and Ahmed M. R. Fath Elbab. Tackling dead end scenarios by improving follow gap method with genetic programming. In 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pages 1566--1571, Nara, Japan, September 2018. [AAET16] Ameen Abdelmutalab, Khaled Assaleh, and Mohamed El-Tarhuni. Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers. Physical Communication, 21:10--18, 2016. [AAFJG11] Alireza Ahangar-Asr, Asaad Faramarzi, Akbar A. Javadi, and Orazio Giustolisi. Modelling mechanical behaviour of rubber concrete using evolutionary polynomial regression. Engineering Computation, 28(4):492--507, 2011. [AAG11] Thomas Ackling, Bradley Alexander, and Ian Grunert. Evolving patches for software repair. In Natalio Krasnogor, Pier Luca Lanzi, Andries Engelbrecht, David Pelta, Carlos Gershenson, Giovanni Squillero, Alex Freitas, Marylyn Ritchie, Mike Preuss, Christian Gagne, Yew Soon Ong, Guenther Raidl, Marcus Gallager, Jose Lozano, Carlos Coello-Coello, Dario Landa Silva, Nikolaus Hansen, Silja Meyer-Nieberg, Jim Smith, Gus Eiben, Ester Bernado-Mansilla, Will Browne, Lee Spector, Tina Yu, Jeff Clune, Greg Hornby, Man-Leung Wong, Pierre Collet, Steve Gustafson, Jean-Paul Watson, Moshe Sipper, Simon Poulding, Gabriela Ochoa, Marc Schoenauer, Carsten Witt, and Anne Auger, editors, GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation, pages 1427--1434, Dublin, Ireland, 12-16 July 2011. ACM. [AAG+22] Rashed Alsharif, Mehrdad Arashpour, Emadaldin Mohammadi Golafshani, M. Reza Hosseini, Victor Chang, and Jenny Zhou. Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings. Journal of Building Engineering, 46:103846, 2022. [AAGA11] Amir Hossein Alavi, Pejman Aminian, Amir Hossein Gandomi, and Milad Arab Esmaeili. Genetic-based modeling of uplift capacity of suction caissons. Expert Systems with Applications, 38(10):12608--12618, 15 September 2011. [AAGM11] Amir Hossein Alavi, Mahmoud Ameri, Amir Hossein Gandomi, and Mohammad Reza Mirzahosseini. Formulation of flow number of asphalt mixes using a hybrid computational method. Construction and Building Materials, 25(3):1338--1355, March 2011. [AAH15] K. P. Amber, M. W. Aslam, and S. K. Hussain. Electricity consumption forecasting models for administration buildings of the UK higher education sector. Energy and Buildings, 90:127--136, 2015. [AAH20] Seyed Mohammad Hossein Hosseini Amini, Mohammad Abdollahi, and Maryam Amir Haeri. Rule-centred genetic programming (RCGP): an imperialist competitive approach. Appl. Intell., 50(8):2589--2609, 2020. [AAH21] Jalal Al-Afandi and Andras Horvath. Adaptive gene level mutation. Algorithms, 14(1), 2021. [AAHM15] Habib Akbari-Alashti, Omid Bozorg Haddad, and Miguel A. Marino. Application of fixed length gene genetic programming (FLGGP) in hydropower reservoir operation. Water Resources Management, 29(9), 2015. [AAJ23a] Umair Ahmed, Fakhre Ali, and Ian Jennions. Acoustic monitoring of an aircraft auxiliary power unit. ISA Transactions, 2023. [AAJ+23b] Muna Albalushi, Rasha Al Jassim, Karan Jetly, Raya Al Khayari, and Hilal Al Maqbali. Optimizing diabetes predictive modeling with automated decision trees. In 2023 IEEE Smart World Congress (SWC), August 2023. [AAJJ23] Alireza Ahangar-Asr, A. Johari, and Akbar A. Javadi. An evolutionary-based polynomial regression modeling approach to predicting discharge flow rate under sheet piles. Engineering with Computers, 39(6):4093--4101, 2023. [AAK04] B. Ali, A. E. A. Almaini, and T. Kalganova. Evolutionary algorithms and theirs use in the design of sequential logic circuits. Genetic Programming and Evolvable Machines, 5(1), March 2004. [AAM+14] Joshua E. Auerbach, Deniz Aydin, Andrea Maesani, Przemyslaw M. Kornatowski, Titus Cieslewski, Gregoire Heitz, Pradeep R. Fernando, Ilya Loshchilov, Ludovic Daler, and Dario Floreano. RoboGen: Robot generation through artificial evolution. In Hiroki Sayama, John Rieffel, Sebastian Risi, Rene Doursat, and Hod Lipson, editors, Proceedings of the Fourteenth International Conference of the Synthesis and Simulation of Living Systems, ALIFE 14, Complex Adaptive Systems, pages 136--137, New York, 30 July-2 August 2014. MIT Press. [AAM19] Daniel Ashlock, Wendy Ashlock, and James Montgomery. Implementing phenotypic plasticity with an adaptive generative representation. In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Siena, Italy, 9-11 July 2019. [AAM23] Mohammed Al-Aghbari and Ashish M. Gujarathi. Hybrid approach of using bi-objective genetic programming in well control optimization of waterflood management. Geoenergy Science and Engineering, 228:211967, 2023. [AAN20] Rida Azmi, Hicham Amar, and Abderrahim Norelyaqine. Generate knowledge base from very high spatial resolution satellite image using robust classification rules and genetic programming. In 2020 IEEE International conference of Moroccan Geomatics (Morgeo), May 2020. [AAN+23] Fadi Althoey, Muhammad Naveed Akhter, Zohaib Sattar Nagra, Hamad Hassan Awan, Fayez Alanazi, Mohsin Ali Khan, Muhammad Faisal Javed, Sayed M. Eldin, and Yasin Onuralp Ozkilic. Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study. Case Studies in Construction Materials, 18:e01774, 2023. [AAO+21] Abiola John Adeyi, Oladayo Adeyi, Emmanuel Olusola Oke, Olusegun Abayomi Olalere, Seun Oyelami, and Akinola David Ogunsola. Effect of varied fiber alkali treatments on the tensile strength of ampelocissus cavicaulis reinforced polyester composites: Prediction, optimization, uncertainty and sensitivity analysis. Advanced Industrial and Engineering Polymer Research, 4(1):29--40, 2021. [AAO+22] Oladayo Adeyi, Abiola J. Adeyi, Emmanuel O. Oke, Bernard I. Okolo, Abayomi O. Olalere, John A. Otolorin, Samuel Okhale, Abiola E. Taiwo, Sunday O. Oladunni, and Kelechi N. Akatobi. Process integration for food colorant production from hibiscus sabdariffa calyx: A case of multi-gene genetic programming (MGGP) model and techno-economics. Alexandria Engineering Journal, 61(7):5235--5252, 2022. [AAP19a] Alok Adhikari, N. Adhikari, and K. C. Patra. Genetic programming: A complementary approach for discharge modelling in smooth and rough compound channels. Journal of The Institution of Engineers (India): Series A, 100(3):395--405, September 2019. [AAP19b] Alok Adhikari, Nibedita Adhikari, and K. C. Patra. Shear force analysis and modeling for discharge estimation using numerical and GP for compound channels. In Soft Computing in Data Analytics. Springer, 2019. [AAPd12] Felipe S. P. Andrade, Jurandy Almeida, Helio Pedrini, and Ricardo da S. Torres. Fusion of local and global descriptors for content-based image and video retrieval. In 17th Iberoamerican Congress on Pattern Recognition, pages 845--853, Buenos Aires, Argentina, 2012. [AAR+04] R. Muhammad Atif Azad, Ali R. Ansari, Conor Ryan, Michael Walsh, and Tim McGloughlin. An evolutionary approach to wall sheer stress prediction in a grafted artery. Applied Soft Computing, 4(2):139--148, May 2004. [AAS09] Dilip Ahalpara, Siddharth Arora, and M Santhanam. Genetic programming based approach for synchronization with parameter mismatches in eeg. In Leonardo Vanneschi, Steven Gustafson, Alberto Moraglio, Ivanoe De Falco, and Marc Ebner, editors, Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009, volume 5481 of LNCS, pages 13--24, Tuebingen, April 15-17 2009. Springer. [AASAR10] Alaa Al-Afeef, Alaa F. Sheta, and Adnan Al-Rabea. Image reconstruction of a metal fill industrial process using genetic programming. In 10th International Conference on Intelligent Systems Design and Applications (ISDA), 2010, pages 12--17, Cairo, 29 November-1 December 2010. [AASB18] Ibrahim Z. Abdelbaky, Ahmed F. Al-Sadek, and Amr A. Badr. Applying machine learning techniques for classifying cyclin-dependent kinase inhibitors. International Journal of Advanced Computer Science and Applications, 9(11):229--235, 2018. [AASP18] Sachindra Dhanapala Arachchige, Khandakar Ahmed, S Shahid, and B. J. C Perera. Cautionary note on the use of genetic programming in statistical downscaling. 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[AASX+19] Shima Afzali, Harith Al-Sahaf, Bing Xue, Christopher Hollitt, and Mengjie Zhang. Genetic programming for feature selection and feature combination in salient object detection. In Paul Kaufmann and Pedro A. Castillo, editors, 22nd International Conference, EvoApplications 2019, volume 11454 of LNCS, pages 308--324, Leipzig, Germany, 24-26 April 2019. Springer Verlag. [AASX+21] Shima Afzali Vahed Moghaddam, Harith Al-Sahaf, Bing Xue, Christopher Hollitt, and Mengjie Zhang. An automatic feature construction method for salient object detection: A genetic programming approach. Expert Systems with Applications, 186:115726, 2021. [AASXZ22a] Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. Automatically diagnosing skin cancers from multimodality images using two-stage genetic programming. IEEE Transactions on Cybernetics, 2022. [AASXZ22b] Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. Genetic programming for automatic skin cancer image classification. Expert Systems with Applications, 197:116680, 2022. [AASXZ23] Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. A new genetic programming representation for feature learning in skin cancer detection. In Sara Silva, Luis Paquete, Leonardo Vanneschi, Nuno Lourenco, Ales Zamuda, Ahmed Kheiri, Arnaud Liefooghe, Bing Xue, Ying Bi, Nelishia Pillay, Irene Moser, Arthur Guijt, Jessica Catarino, Pablo Garcia-Sanchez, Leonardo Trujillo, Carla Silva, and Nadarajen Veerapen, editors, Proceedings of the 2023 Genetic and Evolutionary Computation Conference, GECCO '23, pages 707--710, Lisbon, Portugal, 15-19 July 2023. Association for Computing Machinery. [AASXZ24] Qurrat UI Ain, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. Exploring genetic programming models in Computer-Aided diagnosis of skin cancer images. In Bing Xue, editor, 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, 30 June - 5 July 2024. IEEE. [AAT03] A. F. Ashour, L. F. Alvarez, and V. V. Toropov. Empirical modelling of shear strength of RC deep beams by genetic programming. Computers and Structures, 81(5):331--338, March 2003. [AAT14] Gholamreza Ghodrati Amiri, Mohamad Shamekhi Amiri, and Zahra Tabrizian. Ground motion prediction equations (GMPEs) for elastic response spectra in the iranian plateau using gene expression programming (GEP). Journal of Intelligent and Fuzzy Systems, 26(6):2825--2839, 2014. [AAY19] Joselito Yam II Alcaraz, Kunal Ahluwalia, and Swee-Hock Yeo. Predictive models of Double-Vibropolishing in bowl system using artificial intelligence methods. Journal of Manufacturing and Materials Processing, 3(1), 2019. [AAZ+08] H. Md Azamathulla, A. Ab. Ghani, N. A. Zakaria, S. H. Lai, C. K. Chang, C. S. Leow, and Z. Abuhasan. Genetic programming to predict ski-jump bucket spill-way scour. Journal of Hydrodynamics, Ser. B, 20(4):477--484, August 2008. [AAZG10] H. Md. Azamathulla, Aminuddin Ab Ghani, Nor Azazi Zakaria, and Aytac Guven. Genetic programming to predict bridge pier scour. Journal of Hydraulic Engineering, 136(3):165--169, 2010. [AB98] M. Ahluwalia and L. Bull. Co-evolving functions in genetic programming: Dynamic adf creation using glib. In V. William Porto, N. Saravanan, D. Waagen, and A. E. Eiben, editors, Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming, volume 1447 of LNCS, pages 809--818, Mission Valley Marriott, San Diego, California, USA, 25-27 March 1998. Springer-Verlag. [AB99a] Manu Ahluwalia and Larry Bull. Coevolving functions in genetic programming: Classification using k-nearest-neighbour. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 947--952, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [AB99b] Manu Ahluwalia and Larry Bull. A genetic programming-based classifier system. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 11--18, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [AB00] Douglas A. Augusto and Helio J. C. Barbosa. Symbolic regression via genetic programming. In VI Brazilian Symposium on Neural Networks (SBRN'00), page 173, Rio de Janeiro, RJ, Brazil, January 22-25 2000. IEEE. VI Simposio Brasileiro de Redes Neurais. [AB01] Manu Ahluwalia and Larry Bull. Coevolving functions in genetic programming. Journal of Systems Architecture, 47(7):573--585, July 2001. [AB03] Daniel A. Ashlock and Kenneth M. Bryden. Thermal agents: An application of genetic programming to virtual engineering. In Ruhul Sarker, Robert Reynolds, Hussein Abbass, Kay Chen Tan, Bob McKay, Daryl Essam, and Tom Gedeon, editors, Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, pages 1340--1347, Canberra, 8-12 December 2003. IEEE Press. [AB06] Daniel Ashlock and Kenneth M. Bryden. Function stacks, GBEAs, and crossover for the parity problem. In Cihan H. Dagli, Anna L. Buczak, David L. Enke, Mark Embrechts, and Okan Ersoy, editors, ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks, volume 16, St. Louis, MO, USA, November 5-8 2006. Part I: Evolutionary Computation. [AB10] Deepa Anand and K. K. Bharadwaj. Adaptive user similarity measures for recommender systems: A genetic programming approach. In 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010), volume 8, pages 121--125, 9-11 July 2010. [AB13] Douglas A. Augusto and Helio J. C. Barbosa. Accelerated parallel genetic programming tree evaluation with OpenCL. Journal of Parallel and Distributed Computing, 73(1):86--100, 2013. Metaheuristics on GPUs. [AB16] Daniel A. Ashlock and Joseph Alexander Brown. Evolutionary partitioning regression with function stacks. In Yew Song Ong, editor, Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016), pages 1469--1476, Vancouver, 25-29 July 2016. IEEE Press. [AB23a] Nikola Andelic and Sandi Baressi Segota. Development of symbolic expressions ensemble for breast cancer type classification using genetic programming symbolic classifier and decision tree classifier. Cancers, 15(13):article no. 3411, 29 June 2023. [AB23b] Nikola Andelic and Sandi Baressi Segota. Generating mathematical expressions for estimation of atomic coordinates of carbon nanotubes using genetic programming symbolic regression. Technologies, 11(6):Article No. 185, 2023. [AB24a] Nikola Andelic and Sandi Baressi Segota. Achieving high accuracy in android malware detection through genetic programming symbolic classifier. Computers, 13(8):article number: 197, August 2024. [AB24b] Nikola Andelic and Sandi Baressi Segota. An advanced methodology for crystal system detection in li-ion batteries. Electronics, 13(12):article number: 2278, June 2024. [AB24c] Nikola Andelic and Sandi Baressi Segota. Enhancing network intrusion detection: A genetic programming symbolic classifier approach. Information, 15(3):Article No. 154, 2024. [ABA09] Wafa Abdelmalek, Sana Ben Hamida, and Fathi Abid. Selecting the best forecasting-implied volatility model using genetic programming. Journal of Applied Mathematics and Decision Sciences, 2009. [ABA10] Y. Al-Bastaki and W. Awad. GADS and reusability. Journal of Artificial Intelligence, 3(2):67--77, 2010. [Aba22] Tarek Ababsa. A SIMD interpreter for linear genetic programming. In 2022 International Symposium on iNnovative Informatics of Biskra (ISNIB), December 2022. [ABAA16] Marwa Ammar, Souhir Bouaziz, Adel M. Alimi, and Ajith Abraham. Multi-agent architecture for multiaobjective optimization of flexible neural tree. Neurocomputing, 214:307--316, 2016. [ABAG05] Daniel A. Ashlock, Kenneth M. Bryden, Wendy Ashlock, and Stephen P. Gent. Rapid training of thermal agents with single parent genetic programming. In David Corne, Zbigniew Michalewicz, Marco Dorigo, Gusz Eiben, David Fogel, Carlos Fonseca, Garrison Greenwood, Tan Kay Chen, Guenther Raidl, Ali Zalzala, Simon Lucas, Ben Paechter, Jennifier Willies, Juan J. Merelo Guervos, Eugene Eberbach, Bob McKay, Alastair Channon, Ashutosh Tiwari, L. Gwenn Volkert, Dan Ashlock, and Marc Schoenauer, editors, Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 3, pages 2122--2129, Edinburgh, UK, 2-5 September 2005. IEEE Press. [Abb91] R. J. Abbott. Niches as a ga divide-and-conquer strategy. In Art Chapman and Leonard Myers, editors, Proceedings of the Second Annual AI Symposium for the California State University, pages 133--136. 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