Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

Jaws 30

Published: 22 November 2023 Publication History

Abstract

It is 30 years since John R. Koza published “Jaws”, the first book on genetic programming [Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)]. I recount and expand the celebration at GECCO 2022, very briefly summarise some of what the rest of us have done and make suggestions for the next thirty years of GP research.

References

[1]
Koza JR Genetic Programming: On the Programming of Computers by Means of Natural Selection 1992 Cambridge MIT Press
[2]
Darwin C On the Origin of Species by Means of Natural Selection 1859 1985 London John Murray, Penguin classics
[3]
Darwin C Voyage of the Beagle 1839 1989 London Henry Colburn, Penguin classics
[4]
Koza JR Genetic Programming II: Automatic Discovery of Reusable Programs 1994 Cambridge MIT Press
[5]
Koza JR et al. Genetic Programming III: Darwinian Invention and Problem Solving 1999 Cambridge Morgan Kaufmann
[6]
Koza JR et al. Genetic Programming IV: Routine Human-Competitive Machine Intelligence 2003 Dordrecht Kluwer Academic Publishers
[7]
Koza JR and Rice JP Genetic Programming: The Movie 1992 Cambridge MIT Press
[8]
Koza JR Genetic Programming II Videotape: The next generation 1994 Cambridge MIT Press
[9]
Koza JR et al. Genetic Programming III Videotape: Human Competitive Machine Intelligence 1999 San Francisco Morgan Kaufmann
[10]
Koza JR et al. Genetic Programming IV Video: Human-Competitive Machine Intelligence 2003 Dordrecht Kluwer Academic Publishers
[11]
J. Koza, Automated design using Darwinian evolution and genetic programming. Stanford University, EE380: Computer Systems Colloquium (18 Feb 2009). https://www.youtube.com/watch?v=xIoytwJWJP8
[12]
Le Goues C et al. Automated program repair Commun. ACM 2019 62 12 56-65
[13]
Petke J et al. Genetic improvement of software: a comprehensive survey IEEE Trans. Evol. Comput. 2018 22 3 415-432
[14]
W.B. Langdon, J. Petke, Software is not fragile. in Complex Systems Digital Campus E-conference, ed. by P. Parrend et al. CS-DC’15. Proceedings in Complexity, Springer (Sep 30-Oct 1 2015), pp. 203–211., invited talk
[15]
Langdon WB et al. Efficient multi-objective higher order mutation testing with genetic programming J. Syst. Softw. 2010 83 12 2416-2430
[16]
Harrand N et al. A journey among Java neutral program variants Genet. Program Evolvable Mach. 2019 20 4 531-580
[17]
Schulte E et al. Software mutational robustness Genet. Program Evolvable Mach. 2014 15 3 281-312
[18]
Abou Assi R et al. Coincidental correctness in the Defects4J benchmark Softw. Testing, Verif. Reliab. 2019 29 3 e1696
[19]
Danglot B et al. Correctness attraction: a study of stability of software behavior under runtime perturbation Empir. Softw. Eng. 2018 23 4 2086-2119
[20]
M. Monperrus, Principles of antifragile software. in Companion to the First International Conference on the Art, Science and Engineering of Programming. Programming ’17, ACM, New York, NY, USA (2017), pp. 32:1–32:4.
[21]
Petke J et al. Avgeriou P, Zhang D, et al. Software robustness: A survey, a theory, and some prospects ESEC/FSE 2021, Ideas, Visions and Reflections 2021 Athens ACM 1475-1478
[22]
D. Andre, J.R. Koza, Parallel genetic programming on a network of transputers. in Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, ed. by J.P. Rosca. Tahoe City, California, USA (9 Jul 1995), pp. 111–120. http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/andre_1995_parallel.pdf
[23]
Kinnear KE Jr Advances in Genetic Programming 1994 Cambridge MIT Press
[24]
Angeline PJ and Kinnear KE Jr Advances in Genetic Programming 2 1996 Cambridge MIT Press
[25]
Spector L, et al., et al. Spector L, et al., et al. Quantum computing applications of genetic programming, chap. 7 Advances in Genetic Programming 3 1999 Cambridge MIT Press 135-160
[26]
J.R. Koza et al., (eds.), Genetic Programming 1996: Proceedings of the First Annual Conference. MIT Press, Stanford University, CA, USA (28–31 Jul 1996). http://www.genetic-programming.org/gp96proceedings.html
[27]
J.R. Koza et al., (eds.), Genetic Programming 1997: Proceedings of the Second Annual Conference. Morgan Kaufmann, Stanford University, CA, USA (13-16 Jul 1997). http://www.amazon.com/Genetic-Programming-2nd-Conference-Author/dp/1558604839
[28]
J.R. Koza et al., (eds.), Genetic Programming 1998: Proceedings of the Third Annual Conference. Morgan Kaufmann, University of Wisconsin, Madison, WI, USA (22-25 Jul 1998)
[29]
P.J. Angeline et al, (eds.), Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999. IEEE Press, Washington, DC, USA (July 6-9 1999). https://dblp.org/rec/conf/cec/1999.bib
[30]
W. Banzhaf et al., (eds.), GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, Orlando, Florida, USA (13-17 Jul 1999). http://www.amazon.com/exec/obidos/ASIN/1558606114/qid%3D977054373/105-7666192-3217523
[31]
W. Banzhaf et al., (eds.), Genetic Programming, LNCS, vol. 1391. Springer-Verlag, Paris (14-15 Apr 1998).
[32]
Koza JR et al. Evolving inventions Sci. Am. 2003 288 2 52-59
[33]
Koza JR Human-competitive results produced by genetic programming Genet. Programm. Evolvable Mach. 2010 11 3/4 251-284
[34]
Friedberg RM A learning machine: I IBM J. Res. Dev. 1958 2 1 2-13
[35]
T. Kilburn et al., Experiments in machine learning and thinking. in Information Processing, Proceedings of the 1st International Conference on Information Processing. UNESCO, Paris (15-20 Jun 1959), pp. 303–308. https://dblp.org/rec/conf/ifip/KilburnGS59.bib
[36]
A.M. Turing, Intelligent machinery (1948), https://www.npl.co.uk/getattachment/about-us/History/Famous-faces/Alan-Turing/80916595-Intelligent-Machinery.pdf, report for National Physical Laboratory. Reprinted in Ince, D. C. (editor). 1992. Mechanical Intelligence: Collected Works of A. M. Turing. Amsterdam: North Holland. Pages 107127. Also reprinted in Meltzer, B. and Michie, D. (editors). (1969). Machine Intelligence 5. Edinburgh: Edinburgh University Press [278]
[37]
Turing AM Meltzer B and Michie D Intelligent machinery, chap. 1 Machine Intelligence 1969 Edinburgh Edinburgh University Press 3-23
[38]
W.B. Langdon, W. Banzhaf, Repeated patterns in tree genetic programming. inProceedings of the 8th European Conference on Genetic Programming. Lecture Notes in Computer Science, vol. 3447, ed.by M. Keijzer et al. Springer, Lausanne, Switzerland (30 Mar–1 Apr 2005), pp. 190–202.
[39]
Langdon WB and Banzhaf W Repeated patterns in genetic programming Nat. Comput. 2008 7 4 589-613
[40]
Ohno S Evolution by Gene Duplication 1970 Berlin Springer
[41]
Koza JR and Andre D Hunter L and Klein TE A case study where biology inspired a solution to a computer science problem Pacific Symposium on Biocomputing ’96 1996 Singapore World Scientific 500-511
[42]
J.R. Koza, Architecture-altering operations for evolving the architecture of a multipart program in genetic programming. Technical Report STAN-CS-94-1528, Dept. of Computer Science, Stanford University, Stanford, California 94305, USA (Oct 1994). http://www.genetic-programming.com/jkpdf/tr1528.pdf
[43]
S. Forrest et al., A genetic programming approach to automated software repair. in GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, ed. by G. Raidl et al. ACM, Montreal (8-12 Jul 2009), pp. 947–954., gECCO 2019 10-Year Most Influential Paper Award, Best paper
[44]
Carroll L Through the Looking-Glass, and What Alice Found There 1871 London Macmillan
[45]
W. Weimer et al., Automatically finding patches using genetic programming. in International Conference on Software Engineering (ICSE) 2009, ed. by S. Fickas. Vancouver (May 16-24 2009), pp. 364–374.
[46]
C. Le Goues, Automatic Program Repair Using Genetic Programming. Ph.D. thesis, Faculty of the School of Engineering and Applied Science, University of Virginia, USA (May 2013). http://www.cs.virginia.edu/~weimer/students/claire-phd.pdf
[47]
Haraldsson SO, et al., et al. Petke J, et al., et al. Fixing bugs in your sleep: how genetic improvement became an overnight success GI-2017 2017 Berlin ACM 1513-1520
[48]
N. Alshahwan, Industrial experience of genetic improvement in Facebook. in GI-2019, ed. by J. Petke, et al. ICSE workshops proceedings. IEEE, Montreal (28 May 2019), p. 1., invited Keynote
[49]
Kirbas S et al. On the introduction of automatic program repair in Bloomberg IEEE Softw. 2021 38 4 43-51
[50]
Juille H and Pollack JB Angeline PJ and Kinnear KE Jr Massively parallel genetic programming, chap. 17 Advances in Genetic Programming 2 1996 Cambridge MIT Press 339-357
[51]
Thompson A Hardware Evolution Automatic Design of Electronic Circuits in Reconfigurable Hardware by Artificial Evolution 1998 Berlin Springer
[52]
T.G.W. Gordon, Exploiting Development to Enhance the Scalability of Hardware Evolution. Ph.D. thesis, University College, London, UK (Jul 2005). https://discovery.ucl.ac.uk/id/eprint/1444775/
[53]
P.N. Martin, Genetic Programming in Hardware. Ph.D. thesis, University of Essex, University of Essex, Wivenhoe Park, Colchester, UK (Mar 2003). http://www.naiadhome.com/HardwareGeneticProgramming.pdf
[54]
Sekanina L and Vasicek Z Miller JF CGP acceleration using field-programmable gate arrays, chap. 7 Cartesian Genetic Programming. Natural Computing Series 2011 Berlin Springer 217-230
[55]
Goribar-Jimenez C et al. Lozano JA et al. Towards the development of a complete GP system on an FPGA using geometric semantic operators 2017 IEEE Congress on Evolutionary Computation (CEC) 2017 Donostia IEEE 1932-1939
[56]
Langdon WB and Krauss O Genetic improvement of data for maths functions ACM Trans. Evolut. Learn. Optim. 2021 1 2 7
[57]
Owens JD et al. A survey of general-purpose computation on graphics hardware Comput. Gr. Forum 2007 26 1 80-113
[58]
S. Harding, W. Banzhaf, Fast genetic programming on GPUs. in Proceedings of the 10th European Conference on Genetic Programming. Lecture Notes in Computer Science, vol. 4445, ed. by M. Ebner et al. Springer, Valencia, Spain (11-13 Apr 2007), pp. 90–101.
[59]
D.M. Chitty, A data parallel approach to genetic programming using programmable graphics hardware. in GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation. vol. 2, ed. by D. Thierens et al. ACM Press, London (7-11 Jul 2007), pp. 1566–1573.
[60]
H. Juille, J.B. Pollack, Parallel genetic programming and fine-grained SIMD architecture. in Working Notes for the AAAI Symposium on Genetic Programming, ed. by E.V. Siegel, J.R. Koza. AAAI, MIT, Cambridge, MA, USA (10–12 Nov 1995), pp. 31–37. http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-005.pdf
[61]
W.B. Langdon, A SIMD interpreter for genetic programming on GPU graphics cards. Tech. Rep. CSM-470, Department of Computer Science, University of Essex, Colchester, UK (3 Jul 2007). http://cswww.essex.ac.uk/technical-reports/2007/csm_470.pdf
[62]
W.B. Langdon, W. Banzhaf, A SIMD interpreter for genetic programming on GPU graphics cards. in Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008. Lecture Notes in Computer Science, vol. 4971, ed. by M. O’Neill et al. Springer, Naples (26-28 Mar 2008), pp. 73–85.
[63]
W.B. Langdon, A fast high quality pseudo random number generator for graphics processing units. in 2008 IEEE World Congress on Computational Intelligence, ed. by J. Wang. IEEE, Hong Kong (1-6 Jun 2008), pp. 459–465.
[64]
Robilliard D et al. Genetic programming on graphics processing units Genet. Program Evolvable Mach. 2009 10 4 447-471
[65]
Baumes LA et al. EASEA: a generic optimization tool for GPU machines in asynchronous island model Comput. Methods Mater. Sci. 2011 11 3 489-499
[66]
J. Vitola et al., Parallel algorithm for evolvable-based boolean synthesis on gpus. in Third IEEE Latin American Symposium on Circuits and Systems (LASCAS 2012) (29 Feb-2 Mar 2012).
[67]
A. Maghazeh et al., General purpose computing on low-power embedded GPUs: has it come of age? in 2013 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIII), ed. by H. Jeschke. IEEE, Samos, Greece (15-18 Jul 2013).
[68]
Chitty DM Faster GPU-based genetic programming using a two-dimensional stack Soft. Comput. 2017 21 14 3859-3878
[69]
Ono K and Hanada Y Self-organized subpopulation based on multiple features in genetic programming on GPU J. Adv. Comput. Intell. Intell. Inform. 2021 25 2 177-186
[70]
Trujillo L et al. GSGP-CUDA - a CUDA framework for geometric semantic genetic programming SoftwareX 2022 18 101085
[71]
Langdon WB and Harrison AP GP on SPMD parallel graphics hardware for mega bioinformatics data mining Soft. Comput. 2008 12 12 1169-1183
[72]
Langdon WB Distilling GeneChips with genetic programming on the Emerald GPU supercomputer SIGEVOlution Newsl. ACM Spec. Interest Group Genet. Evolut. Comput. 2012 6 1 15-21
[73]
Langdon WB Tsutsui S and Collet P Large scale bioinformatics data mining with parallel genetic programming on graphics processing units, chap. 15 Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series 2013 Berlin Springer 311-347
[74]
Langdon WB Fernandez de Vega F and Cantu-Paz E Large scale bioinformatics data mining with parallel genetic programming on graphics processing units, chap. 5 Parallel and Distributed Computational Intelligence, Studies in Computational Intelligence 2010 Berlin Springer 113-141
[75]
Langdon WB and Banzhaf W Long-term evolution experiment with genetic programming Artif. Life 2022 28 2 173-204
[76]
Langdon WB Genetic programming convergence Genet. Program Evolvable Mach. 2022 23 1 71-104
[77]
Goodfellow I et al. Deep Learning 2016 Cambridge MIT Press
[78]
W. Weimer, From deep learning to human judgments: Lessons for genetic improvement. GI @ GECCO 2022 (9 Jul 2022), http://geneticimprovementofsoftware.com/slides/gi2022gecco/weimer-keynote-gi-gecco-22.pdf, invited keynote
[79]
W.B. Langdon, Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!, Genetic Programming, vol. 1. Kluwer, Boston (1998),
[80]
Teller A Kinnear KE Jr The evolution of mental models, chap. 9 Advances in Genetic Programming 1994 Cambridge MIT Press 199-219
[81]
Jannink J Kinnear KE Jr Cracking and co-evolving randomizers, chap. 20 Advances in Genetic Programming 1994 Cambridge MIT Press 425-443
[82]
D. Andre, Evolution of mapmaking ability: Strategies for the evolution of learning, planning, and memory using genetic programming. in Proceedings of the 1994 IEEE World Congress on Computational Intelligence. vol. 1, IEEE Press, Orlando, Florida, USA (27-29 Jun 1994), pp. 250–255.
[83]
H. Iba et al. Temporal data processing using genetic programming. in Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), ed. by L.J. Eshelman. Morgan Kaufmann, Pittsburgh, PA, USA (15-19 Jul 1995), pp. 279–286. http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/iba_1995_tdpgp.pdf
[84]
T.D. Haynes, R.L. Wainwright, A simulation of adaptive agents in hostile environment. in Proceedings of the 1995 ACM Symposium on Applied Computing, ed. by K.M. George et al. ACM Press, Nashville, USA (1995), pp. 318–323.
[85]
P. Nordin, W. Banzhaf, Evolving Turing-complete programs for a register machine with self-modifying code. in Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), ed. by L.J. Eshelman. Morgan Kaufmann, Pittsburgh, PA, USA (15-19 Jul 1995), pp. 318–325. http://www.cs.mun.ca/~banzhaf/papers/icga95-2.pdf
[86]
Brave S Angeline PJ and Kinnear KE Jr Evolving recursive programs for tree search, chap. 10 Advances in Genetic Programming 2 1996 Cambridge MIT Press 203-220
[87]
A.I. Esparcia Alcazar, K.C. Sharman, Some applications of genetic programming in digital signal processing. in Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996, ed. by J.R. Koza. Stanford Bookstore, Stanford University, CA, USA (28–31 Jul 1996), pp. 24–31.http://www.iti.upv.es/~anna/papers/someappsgp96.ps
[88]
W.S. Bruce, The Application of Genetic Programming to the Automatic Generation of Object-Oriented Programs. Ph.D. thesis, School of Computer and Information Sciences, Nova Southeastern University, 3100 SW 9th Avenue, Fort Lauderdale, Florida 33315, USA (Dec 1995). https://nsuworks.nova.edu/gscis_etd/430/
[89]
A. Ronge, M.G. Nordahl, Genetic programs and co-evolution developing robust general purpose controllers using local mating in two dimensional populations. in Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation. LNCS, vol. 1141, ed. by : H.M. Voigt et al. Springer Verlag, Berlin, Germany (22-26 Sep 1996), pp. 81–90.
[90]
L. Spector, S. Luke, Cultural transmission of information in genetic programming. in Genetic Programming 1996: Proceedings of the First Annual Conference, ed. by J.R. Koza et al. MIT Press, Stanford University, CA, USA (28–31 Jul 1996), pp. 209–214. http://www.cs.gmu.edu/~sean/papers/culture-gp96.pdf
[91]
Raik SE and Browne DG Yao X, Kim JH, and Furuhashi T Evolving state and memory in genetic programming Simulated Evolution and Learning 1997 Berlin Springer
[92]
Edmonds B and Moss S Corne D and Shapiro JL Modelling of boundedly rational agents using evolutionary programming techniques Evolutionary Computing, LNCS 1997 Berlin Springer-Verlag 31-42
[93]
F.H. Bennett III, A multi-skilled robot that recognizes and responds to different problem environments. in Genetic Programming 1997: Proceedings of the Second Annual Conference, ed. by J.R. Koza et al. Morgan Kaufmann, Stanford University, CA, USA (13-16 Jul 1997), pp. 44–51. http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/bennet_1997_msrrrdpe.pdf
[94]
P.J. Angeline, An alternative to indexed memory for evolving programs with explicit state representations. in Genetic Programming 1997: Proceedings of the Second Annual Conference, ed. by J.R. Koza et al. Morgan Kaufmann, Stanford University, CA, USA (13-16 Jul 1997), pp. 423–430
[95]
I.S. Lim, D. Thalmann, Indexed memory as a generic protocol for handling vectors of data in genetic programming. in Fifth International Conference on Parallel Problem Solving from Nature. LNCS, vol. 1498, ed. by A.E. Eiben et al. Springer-Verlag, Amsterdam (27-30 Sep 1998), pp. 325–334.
[96]
A. Trenaman, The Evolution of Autonomous Agents Using Concurrent Genetic Programming. Ph.D. thesis, Department of Computer Science, National University of Ireland, Maynooth, Ireland (Oct 1999), http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/trenaman/at_thesis1.ps.gz
[97]
A. Silva et al., Evolving controllers for autonomous agents using genetically programmed networks. in Genetic Programming, Proceedings of EuroGP’99. LNCS, vol. 1598, ed. by R. Poli et al. Springer-Verlag, Goteborg, Sweden (26-27 May 1999), pp. 255–269.
[98]
B. Andersson et al., Reactive and memory-based genetic programming for robot control. in Genetic Programming, Proceedings of EuroGP’99. LNCS, vol. 1598, ed. by R. Poli et al. Springer-Verlag, Goteborg, Sweden (26-27 May 1999), pp. 161–172.
[99]
P. Martin, Genetic programming for service creation in intelligent networks. in Genetic Programming, Proceedings of EuroGP’2000. LNCS, vol. 1802, ed. by R. Poli et al. Springer-Verlag, Edinburgh (15-16 Apr 2000), pp. 106–120.
[100]
K. Bearpark, Learning and memory in genetic programming. Ph.D. thesis, School of Engineering Sciences, University of Southampton, UK (2000). http://eprints.soton.ac.uk/45930/
[101]
Karlsson R, et al., et al. Cagnoni S, et al., et al. Sound localization for a humanoid robot using genetic programming Real-World Applications of Evolutionary Computing, LNCS 2000 Edinburgh Springer-Verlag 65-76
[102]
M.C. Martin, Visual obstacle avoidance using genetic programming: First results. in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), ed. by L. Spector et al. Morgan Kaufmann, San Francisco, California, USA (7-11 Jul 2001), pp. 1107–1113. http://www.martincmartin.com/Dissertation/VisualObstacleAvoidanceGP.pdf
[103]
S.P. Brumby et al., Evolving forest fire burn severity classification algorithms for multi-spectral imagery. in In Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, Proceedings of SPIE. vol. 4381, ed. by S.S. Shen, M.R. Descour, (2001), pp. 236–245.
[104]
D. Howard et al., The boru data crawler for object detection tasks in machine vision. in Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN. LNCS, vol. 2279, ed. by S. Cagnoni et al. Springer-Verlag, Kinsale, Ireland (3-4 Apr 2002), pp. 222–232.
[105]
K. Imamura et al., N-version genetic programming via fault masking. in Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002. LNCS, vol. 2278, ed. by J.A. Foster et al. Springer-Verlag, Kinsale, Ireland (3-5 Apr 2002), pp. 172–181.
[106]
M. Johnson, Sequence generation using machine language evolved by genetic programming. in Procceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL’02), ed. by L. Wang et al. Orchid Country Club, Singapore (18-22 Nov 2002), p. #1251. http://www.worldcat.org/title/seal02-proceedings-of-the-4th-asia-pacific-conference-on-simulated-evolution-and-learning-november-18-22-2002-orchid-country-club-singapore/oclc/51951214
[107]
M. O’Neill, C. Ryan, Investigations into memory in grammatical evolution. in GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, ed. by A.M. Barry. AAAI, New York (8 Jul 2002), pp. 141–144. http://www.grammatical-evolution.org/gews2002/oneill.ps
[108]
N. Pillay, Using genetic programming for the induction of novice procedural programming solution algorithms. in SAC ’02: Proceedings of the 2002 ACM symposium on Applied computing. ACM Press, Madrid, Spain (Mar 2002), pp. 578–583.
[109]
Quintana MI et al. Morphological algorithm design for binary images using genetic programming Genet. Program Evolvable Mach. 2006 7 1 81-102
[110]
M. Segond et al., Iterative filter generation using genetic programming. in Proceedings of the 9th European Conference on Genetic Programming. Lecture Notes in Computer Science, vol. 3905, ed. by P. Collet et al. Springer, Budapest, Hungary (10 - 12 Apr 2006), pp. 145–153.
[111]
Kim D Iba H A quantitative analysis of memory usage for agent tasks, chap. 14 Frontiers in Evolutionary Robotics 2008 Rijeka IntechOpen 247-274
[112]
Frias-Martinez E and Gobet F Automatic generation of cognitive theories using genetic programming Mind. Mach. 2007 17 3 287-309
[113]
N.F. McPhee, R. Poli, Memory with memory: Soft assignment in genetic programming. in GECCO ’08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, ed. by M. Keijzer et al. ACM, Atlanta, GA, USA (12-16 Jul 2008), pp. 1235–1242.
[114]
Katz G and Peled D Cha S, Choi JY, Kim M, Lee I, and Viswanathan M Genetic programming and model checking: Synthesizing new mutual exclusion algorithms Automated Technology for Verification and Analysis. Lecture Notes in Computer Science 2008 Berlin Springer 33-47
[115]
Withall MS et al. An improved representation for evolving programs Genet. Program Evolvable Mach. 2009 10 1 37-70
[116]
G.C. Wilson, W. Banzhaf, Soft memory for stock market analysis using linear and developmental genetic programming. in GECCO ’09: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, ed. by G. Raidl et al. ACM, Montreal (8-12 Jul 2009), pp. 1633–1640.
[117]
K. Wolfson, M. Sipper, Efficient list search algorithms. in 9th International Conference, Evolution Artificielle, EA 2009. Lecture Notes in Computer Science, vol. 5975, ed. by P. Collet et al. Springer, Strasbourg, France (Oct 26-28 2009), p. 158–169., revised Selected Papers
[118]
M. Hyde, A genetic programming hyper-heuristic approach to automated packing. Ph.D. thesis, School of Computer Science, University of Nottingham, UK (Mar 2010). http://etheses.nottingham.ac.uk/1625/1/mvh_corrected_thesis.pdf
[119]
M. Suchorzewski, Extending genetic programming to evolve perceptron-like learning programs. in 10th International Conference Artificial Intelligence and Soft Computing, ICAISC 2010, Part II. Lecture Notes in Computer Science, vol. 6114, ed. by L. Rutkowski et al. Springer, Zakopane, Poland (Jun 13-17 2010), pp. 221–228.
[120]
A. Agapitos et al., Learning environment models in car racing using stateful genetic programming. in Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games. pp. 219–226. IEEE, Seoul, South Korea (31 Aug–3 Sep 2011).
[121]
Weise T and Tang K Evolving distributed algorithms with genetic programming IEEE Trans. Evol. Comput. 2012 16 2 242-265
[122]
Kala R Multi-robot path planning using co-evolutionary genetic programming Expert Syst. Appl. 2012 39 3 3817-3831
[123]
H. Yim, D. Kim, Evolving internal memory strategies for the woods problems. in 12th International Conference on Control, Automation and Systems (ICCAS 2012), (2012), pp. 366–369. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp= &arnumber=6393463
[124]
K. Igwe, N. Pillay, Automatic programming using genetic programming. in Proceedings of the 2013 Third World Congress on Information and Communication Technologies (WICT 2013), ed. by L.T. Ngo et al. IEEE, Hanoi, Vietnam (15-18 Dec 2013), pp. 337–342.
[125]
Qadir O et al. Hardware architecture of the protein processing associative memory and the effects of dimensionality and quantisation on performance Genet. Program Evolvable Mach. 2014 15 3 245-275
[126]
Szczuko P Genetic programming extension to APF-based monocular human body pose estimation Multimed. Tools Appl. 2014 68 1 177-192
[127]
X. Yuan et al., Making lock-free data structures verifiable with artificial transactions. in Proceedings of the 8th Workshop on Programming Languages and Operating Systems, PLOS 2015. ACM, Monterey, California, USA (4-7 Oct 2015), pp. 39–45.
[128]
Chaumont N and Adami C Evolution of sustained foraging in three-dimensional environments with physics Genet. Program Evolvable Mach. 2016 17 4 359-390
[129]
R. Smith, M. Heywood, A model of external memory for navigation in partially observative visual reinforcement learning tasks. in EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming. LNCS, vol. 11451, ed. by L. Sekanina et al. Springer Verlag, Leipzig, Germany (24-26 Apr 2019), pp. 162–177.
[130]
Kelly S et al. Emergent tangled program graphs in partially observable recursive forecasting and ViZDoom navigation tasks ACM Trans. Evolut. Learn. Optim. 2021 1 3 1-41
[131]
E. Real et al., AutoML-zero: Evolving machine learning algorithms from scratch. in Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, ed. by H. Daume III, A. Singh, PMLR (13–18 Jul 2020), pp. 8007–8019. http://www.human-competitive.org/sites/default/files/automl_zero_humies_competition_entry.txt, winner 2021 HUMIES
[132]
Sulyok C et al. Evolving the process of a virtual composer Nat. Comput. 2019 18 1 47-60
[133]
M. Al Masalma, M. Heywood, Genetic programming with external memory in sequence recall tasks. in Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion, ed. by H. Trautmann et al. GECCO ’22, Association for Computing Machinery, Boston, USA (9-13 Jul 2022), pp. 518–521.
[134]
Langdon WB and Poli R Foundations of Genetic Programming 2002 Berlin Springer-Verlag
[135]
O’Reilly UM and Oppacher F Whitley LD and Vose MD The troubling aspects of a building block hypothesis for genetic programming Foundations of Genetic Algorithms 3 1994 Estes Park Morgan Kaufmann 73-88
[136]
Rosca JP, Ballard DH, et al. Spector L et al. Rooted-tree schemata in genetic programming, chap. 11 Advances in Genetic Programming 3 1999 Cambridge MIT Press 243-271
[137]
Poli R Exact schema theory for genetic programming and variable-length genetic algorithms with one-point crossover Genet. Program Evolvable Mach. 2001 2 2 123-163
[138]
Stephens CR and Poli R Menon A EC theory–" in theory": Towards a unification of evolutionary computation theory, chap. 7 Frontiers of Evolutionary Computation 2004 Boston Kluwer 129-155
[139]
Price GR Selection and covariance Nature 1970 227 520-521
[140]
Altenberg L Kinnear KE Jr The evolution of evolvability in genetic programming, chap. 3 Advances in Genetic Programming 1994 Cambridge MIT Press 47-74
[141]
Ryan C, et al., et al. Deb K, et al., et al. A competitive building block hypothesis Genetic and Evolutionary Computation - GECCO-2004, Part II. Lecture Notes in Computer Science 2004 Seattle Springer-Verlag 654-665
[142]
White DR, et al., et al. Banzhaf W, et al., et al. Modelling genetic programming as a simple sampling algorithm Genetic Programming Theory and Practice XVII 2019 East Lansing Springer 367-381
[143]
J. Miller, What bloat? cartesian genetic programming on Boolean problems. in 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers, ed. by E.D. Goodman. San Francisco, California, USA (9-11 Jul 2001), pp. 295–302. http://www.elec.york.ac.uk/intsys/users/jfm7/gecco2001Late.pdf
[144]
T. Jones, One operator, one landscape. Tech. Rep. SFI TR 95-02-025, Santa Fe Institute (January 1995). http://www.santafe.edu/sfi/publications/Working-Papers/95-02-025.ps
[145]
U.M. O’Reilly, Using a distance metric on genetic programs to understand genetic operators. in IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation. vol. 5, Orlando, Florida, USA (12-15 Oct 1997), pp. 4092–4097.
[146]
Vassilev VK et al. Ghosh A, Tsutsui S, et al. Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application Advances in Evolutionary Computing: Theory and Applications 2003 New York Springer-Verlag 3-44
[147]
W.B. Langdon, M. Harman, Fitness landscape of the Triangle program. in PPSN-2016 Workshop on Landscape-Aware Heuristic Search, ed. by N. Veerapen, G. Ochoa. Edinburgh (17 Sep 2016). http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/rn1605.pdf, also available as UCL RN/16/05
[148]
W.B. Langdon et al., Dissipative polynomials. in 5th Workshop on Landscape-Aware Heuristic Search, ed. by N. Veerapen et al. GECCO 2021 Companion, ACM, Internet (10-14 Jul 2021), pp. 1683–1691.
[149]
F.D. Francone et al., Homologous crossover in genetic programming. in Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, ed. by W. Banzhaf et al. Morgan Kaufmann, Orlando, Florida, USA (13-17 Jul 1999), pp. 1021–1026. http://gpbib.cs.ucl.ac.uk/gecco1999/GP-463.pdf
[150]
Durrett G et al. Beyer HG, Langdon WB, et al. Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics Foundations of Genetic Algorithms 2011 Schwarzenberg ACM 69-80
[151]
Koetzing T et al. The Max problem revisited: the importance of mutation in genetic programming Theoret. Comput. Sci. 2014 545 94-107
[152]
Nguyen A et al. Neumann F, De Jong K, et al. Single- and multi-objective genetic programming: new bounds for weighted order and majority Foundations of Genetic Algorithms 2013 Adelaide ACM 161-172
[153]
Lissovoi A and Oliveto PS On the time and space complexity of genetic programming for evolving boolean conjunctions J. Artif. Intell. Res. 2019 66 655-689
[154]
Doerr B et al. The impact of lexicographic parsimony pressure for ORDER/MAJORITY on the run time Theoret. Comput. Sci. 2020 816 144-168
[155]
Koetzing T et al. Destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming Theoret. Comput. Sci. 2020 816 96-113
[156]
Wolpert DH and Macready WG No free lunch theorems for optimization IEEE Trans. Evol. Comput. 1997 1 1 67-82
[157]
W.B. Langdon, Incremental evaluation in genetic programming. in EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming. LNCS, vol. 12691, ed. by T. Hu et al. Springer Verlag, Virtual Event (7-9 Apr 2021), pp. 229–246.
[158]
S. Handley, On the use of a directed acyclic graph to represent a population of computer programs. in Proceedings of the 1994 IEEE World Congress on Computational Intelligence. vol. 1, IEEE Press, Orlando, Florida, USA (27-29 Jun 1994), pp. 154–159.
[159]
Langdon WB and Banzhaf W Repeated sequences in linear genetic programming genomes Complex Syst. 2005 15 4 285-306
[160]
Soule T and Foster JA Effects of code growth and parsimony pressure on populations in genetic programming Evolut. Comput. 1998 6 4 293-309
[161]
de Jong ED and Pollack JB Multi-objective methods for tree size control Genet. Program Evolvable Mach. 2003 4 3 211-233
[162]
S. Bleuler et al., Multiobjective genetic programming: Reducing bloat using spea2. in Proceedings of the 2001 Congress on Evolutionary Computation CEC2001. IEEE Press, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea (27-30 May 2001), pp. 536–543.
[163]
Panait L, Luke S, et al. Deb K et al. Alternative bloat control methods Genetic and Evolutionary Computation - GECCO-2004, Part II. Lecture Notes in Computer Science 2004 Seattle Springer-Verlag 630-641
[164]
R. Poli, A simple but theoretically-motivated method to control bloat in genetic programming. in Genetic Programming, Proceedings of EuroGP’2003. LNCS, vol. 2610, ed. by C. Ryan et al. Springer-Verlag, Essex (14-16 Apr 2003), pp. 204–217.
[165]
S. Silva, Controlling Bloat: Individual and Population Based Approaches in Genetic Programming. Ph.D. thesis, Coimbra University, Portugal (Apr 2008). http://hdl.handle.net/10316/8542, full author name is Sara Guilherme Oliveira da Silva
[166]
S. Dignum, R. Poli, Operator equalisation and bloat free GP. in Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008. Lecture Notes in Computer Science, vol. 4971, ed. by M. O’Neill et al. Springer, Naples (26-28 Mar 2008), pp. 110–121.
[167]
Nikolaev NI and Iba H Accelerated genetic programming of polynomials Genet. Program Evolvable Mach. 2001 2 3 231-257
[168]
Kushchu I Genetic programming and evolutionary generalization IEEE Trans. Evol. Comput. 2002 6 5 431-442
[169]
Kowaliw T and Doursat R Bias-variance decomposition in genetic programming Open Math. 2016 14 1 62-80
[170]
Gathercole C, Ross P, et al. Davidor Y et al. Dynamic training subset selection for supervised learning in genetic programming Parallel Problem Solving from Nature III. LNCS 1994 Jerusalem Springer-Verlag 312-321
[171]
Spector L, et al., et al. Banzhaf W, et al., et al. Relaxations of lexicase parent selection Genetic Programming Theory and Practice XV. Genetic and Evolutionary Computation 2017 Cham Springer 105-120
[172]
Javed N et al. Simplification of genetic programs: a literature survey Data Min. Knowl. Discov. 2022 36 4 1279-1300
[173]
D. Hooper, N.S. Flann, Improving the accuracy and robustness of genetic programming through expression simplification. in Genetic Programming 1996: Proceedings of the First Annual Conference, ed. by J.R. Koza et al. MIT Press, Stanford University, CA, USA (28–31 Jul 1996), p. 428. http://digital.cs.usu.edu/~flann/gp.pdf
[174]
Langdon WB and Harman M Optimising existing software with genetic programming IEEE Trans. Evol. Comput. 2015 19 1 118-135
[175]
Raichle ME and Gusnard DA Appraising the brain’s energy budget Proc. Natl. Acad. Sci. 2002 99 16 10237-10239
[176]
M. Ridley, The Red Queen, Sex and the Evolution of Human Nature. Penquin (1993). http://www.penguin.co.uk/Penguin/Books/0140167722.html
[177]
Nordin P Kinnear KE Jr A compiling genetic programming system that directly manipulates the machine code, chap. 14 Advances in Genetic Programming 1994 Cambridge MIT Press 311-331
[178]
J.F. Miller, An empirical study of the efficiency of learning Boolean functions using a cartesian genetic programming approach. in Proceedings of the Genetic and Evolutionary Computation Conference. vol. 2, ed. by W. Banzhaf et al. Morgan Kaufmann, Orlando, Florida, USA (13-17 Jul 1999), pp. 1135–1142. http://citeseer.ist.psu.edu/153431.html
[179]
C. Ryan et al., Grammatical evolution: Evolving programs for an arbitrary language. in Proceedings of the First European Workshop on Genetic Programming. LNCS, vol. 1391, ed. by W. Banzhaf et al. Springer-Verlag, Paris (14-15 Apr 1998), pp. 83–96.
[180]
P.J. Angeline, J.B. Pollack, The evolutionary induction of subroutines. in Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society. pp. 236–241. Lawrence Erlbaum, Bloomington, Indiana, USA (1992), http://www.demo.cs.brandeis.edu/papers/glib92.pdf
[181]
J. Rosca, Towards automatic discovery of building blocks in genetic programming. in Working Notes for the AAAI Symposium on Genetic Programming, ed. by E.V. Siegel, J.R. Koza. AAAI, MIT, Cambridge, MA, USA (10–12 Nov 1995), pp. 78–85. http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-011.pdf
[182]
Spector L Angeline PJ and Kinnear KE Jr Simultaneous evolution of programs and their control structures, chap. 7 Advances in Genetic Programming 2 1996 Cambridge MIT Press 137-154
[183]
G. Murphy, C. Ryan, Seeding methods for run transferable libraries. in GECCO ’07: Proceedings of the 9th annual conference on Genetic and Evolutionary Computation. vol. 2,ed. by D. Thierens et al. ACM Press, London (7-11 Jul 2007), pp. 1755–1755.
[184]
W.B. Langdon, Data Structures and Genetic Programming. Ph.D. thesis, University College, London, UK (27 Sep 1996), http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon.ps.gz
[185]
A. Teller, D. Andre, Automatically choosing the number of fitness cases: The rational allocation of trials. in Genetic Programming 1997: Proceedings of the Second Annual Conference, ed. by J.R. Koza et al. Morgan Kaufmann, Stanford University, CA, USA (13-16 Jul 1997), pp. 321–328. http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/GR.ps
[186]
Spector L McClymont K and Keedwell E Assessment of problem modality by differential performance of lexicase selection in genetic programming: a preliminary report 1st workshop on Understanding Problems (GECCO-UP) 2012 Philadelphia ACM 401-408
[187]
R. Poli et al., A field guide to genetic programming. Published via http://lulu.com and freely available at: http://www.gp-field-guide.org.uk (2008), http://www.gp-field-guide.org.uk, (With contributions by J. R. Koza)
[188]
Harding SL, Banzhaf W, et al. Hidalgo I et al. Distributed genetic programming on GPUs using CUDA Workshop on Parallel Architectures and Bioinspired Algorithms 2009 Raleigh Universidad Complutense de Madrid 1-10
[189]
R.L. Crepeau, Genetic evolution of machine language software. in Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, ed. by J.P. Rosca. Tahoe City, California, USA (9 Jul 1995), pp. 121–134. http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GEMS_Article.pdf
[190]
Nordin P, et al., et al. Spector L, et al., et al. Efficient evolution of machine code for CISC architectures using instruction blocks and homologous crossover, chap. 12 Advances in Genetic Programming 3 1999 Cambridge MIT Press 275-299
[191]
F.D. Francone, Discipulus Owner’s Manual. 11757 W. Ken Caryl Avenue F, PBM 512, Littleton, Colorado, 80127-3719, USA, version 3.0 draft edn. (2001). http://gpbib.cs.ucl.ac.uk/gp-html/francone_manual.html
[192]
Banzhaf W et al. Genetic Programming-An Introduction;On the Automatic Evolution of Computer Programs and its Applications 1998 San Francisco Morgan Kaufmann
[193]
Brameier M and Banzhaf W Linear Genetic Programming. No. XVI in Genetic and Evolutionary Computation 2007 Berlin Springer
[194]
O’Neill M and Ryan C Grammatical evolution IEEE Trans. Evol. Comput. 2001 5 4 349-358
[195]
O’Neill M and Ryan C Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language, Genetic programming 2003 Dordrecht Kluwer Academic Publishers
[196]
L. Spector, Introduction to the peer commentary special section on “on the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genetic Programming and Evolvable Machines 18(3), 351–352 (Sep 2017)., special Peer Commentary on Mapping of Genotype to Phenotype in Evolutionary Algorithms
[197]
C. Ryan, A rebuttal to whigham, dick, and maclaurin by one of the inventors of grammatical evolution: Commentary on “on the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genetic Programming and Evolvable Machines 18(3), 385–389 (Sep 2017)., special Peer Commentary on Mapping of Genotype to Phenotype in Evolutionary Algorithms
[198]
Miller JF et al. Principles in the evolutionary design of digital circuits-part I Genet. Program Evolvable Mach. 2000 1 1/2 7-35
[199]
Miller JF et al. Principles in the evolutionary design of digital circuits-part II Genet. Program Evolvable Mach. 2000 1 3 259-288
[200]
Miller JF Cartesian Genetic Programming. Natural Computing Series 2011 Berlin Springer
[201]
Miller JF Cartesian Genetic Programming: its status and future Genetic Programming and Evolvable Machines 2020 21 1–2 129-168
[202]
L. Sekanina, Z. Vasicek, Approximate circuit design by means of evolvable hardware. in IEEE International Conference on Evolvable Systems (ICES 2013). (Apr 2013), pp. 21–28.
[203]
Sekanina L et al. Approximate circuits in low-power image and video processing: The approximate median filter Radioengineering 2017 26 3 623-632
[204]
Montana DJ Strongly typed genetic programming Evolutionary Computation 1995 3 2 199-230
[205]
T. Yu, Structure abstraction and genetic programming. in Proceedings of the Congress on Evolutionary Computation. vol. 1, ed. by P.J. Angeline et al. IEEE Press, Mayflower Hotel, Washington D.C., USA (6-9 Jul 1999), pp. 652–659.
[206]
Yu T Hierachical processing for evolving recursive and modular programs using higher order functions and lambda abstractions Genet. Program Evolvable Mach. 2001 2 4 345-380
[207]
McKay RI et al. Grammar-based genetic programming: a survey Genet. Program. Evolvable Mach. 2010 11 3/4 365-396
[208]
P.A. Whigham, Grammatically-based genetic programming. in Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, ed. by J.P. Rosca. Tahoe City, California, USA (9 Jul 1995), pp. 33–41. http://divcom.otago.ac.nz/sirc/Peterw/Publications/ml95.zip
[209]
Whigham PA et al. On the mapping of genotype to phenotype in evolutionary algorithms Genet. Program. Evolvable Mach. 2017 18 3 353-361
[210]
A. Ratle, M. Sebag, A novel approach to machine discovery: Genetic programming and stochastic grammars. in Proceedings of Twelfth International Conference on Inductive Logic Programming. LNCS, vol. 2583, ed. by S. Matwin, C. Sammut. Springer Verlag, Sydney, Australia (Jul 9-11 2002), pp. 207–222., revised Papers
[211]
Nguyen XH et al. Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: the comparative results Aust. J. Intell. Inform. Process. Syst. 2001 7 3/4 114-121
[212]
Jacob C Evolution and coevolution of developmental programs Comput. Phys. Commun. 1999 121–122 46-50
[213]
Jacob C Illustrating Evolutionary Computation with Mathematica 2001 Cambridge Morgan Kaufmann
[214]
Hornby GS and Pollack JB Evolving L-systems to generate virtual creatures Comput. Graph. 2001 25 6 1041-1048 artificial Life
[215]
M. Hemberg et al., Genr8: Architects’ experience with an emergent design tool, in The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, chap. 8. ed. by J. Romero, P. Machado (Springer, 2008), pp.167–188.
[216]
T. Perkis, Stack-based genetic programming. in Proceedings of the 1994 IEEE World Congress on Computational Intelligence. vol. 1, pp. 148–153. IEEE Press, Orlando, Florida, USA (27-29 Jun 1994).
[217]
Openshaw S and Turton I Fogarty TC Building new spatial interaction models using genetic programming Evolutionary Computing 1994 Leeds, UK AISB workshop
[218]
K. Holladay et al., Fifth: A stack based gp language for vector processing. in Proceedings of the 10th European Conference on Genetic Programming. Lecture Notes in Computer Science, vol. 4445, ed. by M. Ebner et al. pp. 102–113. Springer, Valencia, Spain (11-13 Apr 2007).
[219]
Oltean M, Grosan C, et al. Berthold MR et al. Solving classification problems using infix form genetic programming Advances in Intelligent Data Analysis V. Lecture Notes in Computer Science 2003 Berlin Springer 242-253
[220]
Spector L and Robinson A Genetic programming and autoconstructive evolution with the push programming language Genet. Program Evolvable Mach. 2002 3 1 7-40
[221]
O’Reilly UM, Oppacher F, et al. Davidor Y et al. Program search with a hierarchical variable length representation: genetic programming, simulated annealing and hill climbing Parallel Problem Solving from Nature - PPSN III. Lecture Notes in Computer Science 1994 Jerusalem Springer-Verlag 397-406
[222]
A.I. Esparcia-Alcazar, K.C. Sharman, Genetic programming techniques that evolve recurrent neural networks architectures for signal processing. in IEEE Workshop on Neural Networks for Signal Processing. IEEE, Seiko, Kyoto, Japan (4-6 Sep 1996), pp. 139–148.
[223]
A. Moraglio, S. Silva, Geometric differential evolution on the space of genetic programs. in Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010. LNCS, vol. 6021, ed. by A.I. Esparcia-Alcazar et al. Springer, Istanbul (7-9 Apr 2010), pp. 171–183., best paper
[224]
Zhang BT Bayesian methods for efficient genetic programming Genet. Program Evolvable Mach. 2000 1 3 217-242
[225]
K. Yanai, H. Iba, Estimation of distribution programming based on Bayesian network. in Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, ed. by R. Sarker et al. IEEE Press, Canberra (8-12 Dec 2003), pp. 1618–1625.
[226]
Bosman PAN, de Jong ED, et al. Yao X et al. Learning probabilistic tree grammars for genetic programming Parallel Problem Solving from Nature - PPSN VIII. LNCS 2004 Birmingham Springer-Verlag 192-201
[227]
A. Rodriguez, A Neat Approach To Genetic Programming. Master’s thesis, School of School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida, USA (2007). https://stars.library.ucf.edu/etd/3323.pdf
[228]
Z. Buk et al., NEAT in HyperNEAT substituted with genetic programming. in 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009. Lecture Notes in Computer Science, vol. 5495, ed. by M. Kolehmainen et al. Springer, Kuopio, Finland (23-25 Apr 2009), pp. 243–252., revised selected papers
[229]
Trujillo L et al. neat genetic programming: Controlling bloat naturally Inf. Sci. 2016 333 21-43
[230]
McConaghy T et al. Riolo R et al. FFX: fast, scalable, deterministic symbolic regression technology, chap. 13 Genetic Programming Theory and Practice IX. Genetic and Evolutionary Computation 2011 Ann Arbor Springer 235-260
[231]
Moraglio A, et al., et al. Coello Coello CA, et al., et al. Geometric semantic genetic programming Parallel Problem Solving from Nature, PPSN XII (part 1). Lecture Notes in Computer Science 2012 Taormina Springer 21-31
[232]
W.B. Langdon, Directed crossover within genetic programming. Research Note RN/95/71, University College London, Gower Street, London WC1E 6BT, UK (Sep 1995), http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/directed_crossover.pdf
[233]
P. Orzechowski et al., Where are we now?: a large benchmark study of recent symbolic regression methods. in GECCO ’18: Proceedings of the Genetic and Evolutionary Computation Conference, ed. by H. Aguirre et al. ACM, Kyoto, Japan (15-19 Jul 2018), pp. 1183–1190.
[234]
I. Arnaldo et al., Multiple regression genetic programming. in GECCO ’14: Proceedings of the 2014 conference on Genetic and evolutionary computation, ed. by C. Igel et al. ACM, Vancouver, BC, Canada (12-16 Jul 2014), pp. 879–886.
[235]
L. Munoz et al., M3GP: multiclass classification with GP. in 18th European Conference on Genetic Programming. LNCS, vol. 9025, ed. by P. Machado et al. Springer, Copenhagen (8-10 Apr 2015), pp. 78–91.
[236]
W. La Cava, J. Moore, A general feature engineering wrapper for machine learning using epsilon-lexicase survival. in EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming. LNCS, vol. 10196, ed. by M. Castelli et al. Springer Verlag, Amsterdam (19-21 Apr 2017), pp. 80–95.
[237]
B. Burlacu et al., Operon C++: An efficient genetic programming framework for symbolic regression. in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, ed. by R. Allmendinger et al. GECCO ’20, Association for Computing Machinery, internet (Jul 8-12 2020), pp. 1562–1570.
[238]
Mota Dias D, et al., et al. Nedjah N, et al., et al. Automatic synthesis of microcontroller assembly code through linear genetic programming Genetic Systems Programming: Theory and Experiences, Studies in Computational Intelligence 2006 Germany Springer 193-227
[239]
Lewis TE, Magoulas GD, et al. Harding S et al. TMBL kernels for CUDA GPUs compile faster using PTX GECCO 2011 Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU) 2011 Dublin ACM 455-462
[240]
Cupertino LF, et al., et al. Harding S, et al., et al. Evolving CUDA PTX programs by quantum inspired linear genetic programming GECCO 2011 Computational intelligence on Consumer Games and Graphics Hardware (CIGPU) 2011 Dublin ACM 399-406
[241]
M. Gregor, J. Spalek, Using LLVM-based JIT compilation in genetic programming. In: 2016 ELEKTRO. pp. 406–411. IEEE, Strbske Pleso, Slovakia (16-18 May 2016).
[242]
Liou JY et al. GEVO: GPU code optimization using evolutionary computation ACM Trans. Archit. Code Optim. 2020 17 4 33
[243]
E. Lukschandl et al., Distributed java bytecode genetic programming. in Genetic Programming, Proceedings of EuroGP’2000. LNCS, vol. 1802, ed. by R. Poli et al. Springer-Verlag, Edinburgh (15-16 Apr 2000), pp. 316–325.
[244]
Whigham PA and McKay RI Yao X Genetic approaches to learning recursive relations Progress in Evolutionary Computation. Lecture Notes in Artificial Intelligence 1995 Berlin Springer-Verlag 17-27
[245]
P.A. Whigham, A schema theorem for context-free grammars. in 1995 IEEE Conference on Evolutionary Computation. vol. 1, pp. 178–181. IEEE Press, Perth, Australia (29 Nov - 1 Dec 1995).
[246]
T. Castle, C.G. Johnson, Evolving high-level imperative program trees with strongly formed genetic programming. in Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012. LNCS, vol. 7244, ed. by A. Moraglio et al. Springer Verlag, Malaga, Spain (11-13 Apr 2012), pp. 1–12.
[247]
Hillis WD et al. Langton CG et al. Co-evolving parasites improve simulated evolution as an optimization procedure Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity 1992 Santa Fe Institute Addison-Wesley 313-324
[248]
Popovici E, et al., et al. Rozenberg G, et al., et al. Coevolutionary principles, chap. 31 Handbook of Natural Computing 2012 Berlin Springer 987-1033
[249]
Zhang BT, Cho DY, et al. Spector L et al. Coevolutionary fitness switching: Learning complex collective behaviors using genetic programming, chap. 18 Advances in Genetic Programming 3 1999 Cambridge MIT Press 425-445
[250]
A. Leier, W. Banzhaf, Exploring the search space of quantum programs. in Proceedings of the 2003 Congress on Evolutionary Computation CEC2003. vol. 1, ed. by R. Sarker et al. IEEE Press, Canberra (8-12 Dec 2003), pp. 170–177.
[251]
Spector L Automatic Quantum Computer Programming: A Genetic Programming Approach, Genetic Programming 2004 Boston Kluwer Academic Publishers
[252]
G. O’Brien, J. Clark, Using genetic improvement to retarget quantum software on differing hardware. In: Petke, J., et al. (eds.) GI @ ICSE 2021. IEEE, internet (30 May 2021), pp. 31–38., winner Best Presentation
[253]
Poli R et al. Theoretical results in genetic programming: the next ten years? Genet. Program. Evolvable Mach. 2010 11 3/4 285-320
[254]
Vanneschi L, Poli R, et al. Rozenberg G et al. Genetic programming: introduction, applications, theory and open issues, chap. 24 Handbook of Natural Computing 2012 Berlin Springer 709-739
[255]
A. Marginean et al., SapFix: automated end-to-end repair at scale. in 41st International Conference on Software Engineering, ed. by J.M. Atlee, T. Bultan, ACM, Montreal (25-31 May 2019), ACM, Montreal (25-31 May 2019), pp. 269-278.
[256]
Bruce BR et al. Approximate oracles and synergy in software energy search spaces IEEE Trans. Software Eng. 2019 45 11 1150-1169
[257]
F. Wu et al., Deep parameter optimisation. in GECCO ’15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, ed. by S. Silva et al. ACM, Madrid (11-15 Jul 2015), pp. 1375–1382.
[258]
W.B. Langdon, M. Harman, Genetically improved CUDA C++ software. in 17th European Conference on Genetic Programming. LNCS, vol. 8599, ed. by M. Nicolau et al. Springer, Granada, Spain (23-25 Apr 2014), pp. 87–99.
[259]
W.B. Langdon et al., Improving 3D medical image registration CUDA software with genetic programming. in GECCO ’14: Proceeding of the sixteenth annual conference on genetic and evolutionary computation conference, ed. by C. Igel et al. ACM, Vancouver, BC, Canada (12-15 Jul 2014), pp. 951–958.
[260]
Langdon WB, Harman M, et al. Langdon WB et al. Grow and graft a better CUDA pknotsRG for RNA pseudoknot free energy calculation Genetic Improvement 2015 Workshop 2015 Madrid ACM 805-810
[261]
Yeboah-Antwi K, Baudry B, et al. Langdon WB et al. Embedding adaptivity in software systems using the ECSELR framework Genetic Improvement 2015 Workshop 2015 Madrid ACM 839-844
[262]
W.B. Langdon et al., Improving CUDA DNA analysis software with genetic programming. in GECCO ’15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, ed. by S. Silva et al. ACM, Madrid (11-15 Jul 2015), pp. 1063–1070.
[263]
Langdon WB et al. Genetic improvement of GPU software Genet. Program Evolvable Mach. 2017 18 1 5-44
[264]
Langdon WB et al. Gandomi AH et al. Genetically improved software, chap. 8 Handbook of Genetic Programming Applications 2015 Berlin Springer 181-220
[265]
Langdon WB and Lam BYH Genetically improved BarraCUDA BioData Mining 2017
[266]
W.B. Langdon et al., Evolving better RNAfold structure prediction. in EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming. LNCS, vol. 10781, ed. by M. Castelli et al. Springer Verlag, Parma, Italy (4-6 Apr 2018), pp. 220–236.
[267]
W.B. Langdon, M. Harman, Evolving a CUDA kernel from an nVidia template. in 2010 IEEE World Congress on Computational Intelligence, ed. by P. Sobrevilla. IEEE, Barcelona (18-23 Jul 2010), pp. 2376–2383.
[268]
J.Y. Liou et al., Genetic improvement of GPU code. in GI-2019, ICSE workshops proceedings, ed. by J. Petke et al. IEEE, Montreal (28 May 2019), pp. 20–27., best Paper
[269]
E.T. Barr et al., Automated software transplantation. in International Symposium on Software Testing and Analysis, ISSTA 2015, ed. by T. Xie, M. Young. ACM, Baltimore, Maryland, USA (14-17 Jul 2015), pp. 257–269., ACM SIGSOFT Distinguished Paper Award
[270]
Burke EK et al. Mumford CL, Jain LC, et al. Exploring hyper-heuristic methodologies with genetic programming, chap. 6 Computational Intelligence, Intelligent Systems Reference Library 2009 Springer Berlin 177-201
[271]
R.S. Olson, J.H. Moore, TPOT: A tree-based pipeline optimization tool for automating data science. In: Hutter, F., et al. (eds.) AutoML 2016 workshop. New York City, USA (Jun 24 2016), https://docs.google.com/viewer?a=v &pid=sites &srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE2fGd4OmFmYjMyNWU2NWI1YTBmZg, collocated with ICML
[273]
Krawiec K and Liskowski P Adaptive test selection for factorization-based surrogate fitness in genetic programming Found. Comput. Decis. Sci. 2017 42 4 339-358
[274]
Johnson CG Solving the Rubik’s cube with stepwise deep learning Expert Syst.: J. Knowl. Eng. 2021 38 3
[275]
Langdon WB Evolving open complexity SIGEVOlution Newsl. ACM Spec. Interest Group Genet. Evolut. Comput. 2022 15 1 1-4
[276]
S. Forrest, Engineering and evolving software (2021).
[277]
Moore GE Cramming more components onto integrated circuits Electronics 1965 38 8 114-117
[278]
S. Wright, The roles of mutation, inbreeding, crossbreeding and selection in evolution. in Proceedings of the Sixth Annual Congress of Genetics. pp. 356–366 (1932). http://www.blackwellpublishing.com/ridley/classictexts/wright.pdf

Cited By

View all
  • (2024)GSGP-hardware: instantaneous symbolic regression with an FPGA implementation of geometric semantic genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-024-09491-525:2Online publication date: 1-Dec-2024
  • (2023)Is the evolution metaphor still necessary or even useful for genetic programming?Genetic Programming and Evolvable Machines10.1007/s10710-023-09469-924:2Online publication date: 22-Nov-2023

Index Terms

  1. Jaws 30
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      cover image Genetic Programming and Evolvable Machines
      Genetic Programming and Evolvable Machines  Volume 24, Issue 2
      Dec 2023
      334 pages

      Publisher

      Kluwer Academic Publishers

      United States

      Publication History

      Published: 22 November 2023

      Author Tags

      1. Genetic programming
      2. Genetic improvement
      3. Modularity
      4. Scaling
      5. Parallel computing

      Qualifiers

      • Research-article

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 14 Dec 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)GSGP-hardware: instantaneous symbolic regression with an FPGA implementation of geometric semantic genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-024-09491-525:2Online publication date: 1-Dec-2024
      • (2023)Is the evolution metaphor still necessary or even useful for genetic programming?Genetic Programming and Evolvable Machines10.1007/s10710-023-09469-924:2Online publication date: 22-Nov-2023

      View Options

      View options

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media