Abstract
The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called machine intelligence (Turing 1948, 1950).
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References
Abbass H, Hoai N, McKay R (2002) AntTAG: a new method to compose computer programs using colonies of ants. In: Proceedings of the CEC 2002, Honolulu, pp 1654–1659
Achilles A-C, Ortyl P (1995–2013) The collection of computer science bibliographies. Avaliable from http://liinwww.ira.uka.de/bibliography/
Andre D, Teller A (1999) Evolving team Darwin united. In: Asada M, Kitano H (eds) RoboCup-98: robot soccer world cup II. LNCS 1604. Springer, Berlin, pp 346–351
Andre D, Bennett FH III, Koza JR (1996) Discovery by genetic programming of a cellular automata rule that is better than any known rule for the majority classification problem. In: Koza JR et al (eds) Proceedings of the 1st annual conference on genetic programming, Stanford. MIT, Cambridge, pp 3–11
Angeline PJ (1996) An investigation into the sensitivity of genetic programming to the frequency of leaf selection during subtree crossover. In: Koza JR et al (eds) Proceedings of the 1st annual conference on genetic programming, Stanford. MIT, Cambridge, pp 21–29
Angeline PJ, Kinnear KE Jr (eds) (1996) Advances in genetic programming 2. MIT, Cambridge
Angeline PJ, Pollack JB (1992) The evolutionary induction of subroutines. In: Proceedings of the 14th annual conference of the cognitive science society. Lawrence Erlbaum, Abingdon, Indiana University, Bloomington, pp 236–241
Azaria Y, Sipper M (2005a) GP-gammon: genetically programming backgammon players. Genet Program Evol Mach 6:283–300. Published online: 12 Aug 2005
Azaria Y, Sipper M (2005b) GP-gammon: using genetic programming to evolve backgammon players. In: Keijzer M et al (eds) Proceedings of the 8th European conference on genetic programming, Lausanne. LNCS 3447. Springer, Berlin, pp 132–142
Babovic V (1996) Emergence, evolution, intelligence; hydroinformatics—a study of distributed and decentralised computing using intelligent agents. AA Balkema, Rotterdam
Balasubramaniam P, Kumar AVA (2009) Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming. Genet Program Evol Mach 10:71–89
Balic J (1999) Flexible manufacturing systems; development–structure–operation–handling–tooling. Manufacturing technology. DAAAM International, Vienna
Baluja S, Caruana R (1995) Removing the genetics from the standard genetic algorithm. In: Prieditis A, Russell S (eds) Proceedings of the 12th international conference on machine learning, Tahoe City. Morgan Kaufmann, San Francisco, pp 38–46
Banzhaf W, Langdon WB (2002) Some considerations on the reason for bloat. Genet Program Evol Mach 3:81–91
Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming—an introduction; on the automatic evolution of computer programs and its applications. Morgan Kaufmann, San Francisco
Barnum H, Bernstein HJ, Spector L (2000) Quantum circuits for OR and AND of ORs. J Phys A 33:8047–8057
Bennett FH III, Koza JR, Keane MA, Yu J, Mydlowec W, Stiffelman O (1999) Evolution by means of genetic programming of analog circuits that perform digital functions. In: Banzhaf W et al (eds) GECCO 1999, Orlando, vol 2. Morgan Kaufmann, San Mateo, pp 1477–1483
Bhanu B, Lin Y, Krawiec K (2005) Evolutionary synthesis of pattern recognition systems. Monographs in computer science. Springer, New York
Blickle T (1996) Theory of evolutionary algorithms and application to system synthesis. PhD thesis, Swiss Federal Institute of Technology, Zurich
Bongard J, Lipson H (2007) Automated reverse engineering of nonlinear dynamical systems. Proc Natl Acad Sci 104:9943–9948
Brabazon A, O’Neill M (2006) Biologically inspired algorithms for financial modelling. Natural computing series. Springer, Berlin
Brameier M, Banzhaf W (2007) Linear genetic programming. Genetic and evolutionary computation series, vol XVI. Springer, Berlin
Brezocnik M (2000) Uporaba genetskega programiranja v inteligentnih proizvodnih sistemih. University of Maribor, Slovenia
Chen S-H (ed) (2002) Genetic algorithms and genetic programming in computational finance. Kluwer, Dordrecht
Corno F, Sanchez E, Squillero G (2005) Evolving assembly programs: how games help microprocessor validation. IEEE Trans Evol Comput 9:695–706
Crawford-Marks R, Spector L (2002) Size control via size fair genetic operators in the PushGP genetic programming system. In: Langdon WB et al (eds) GECCO 2002, New York. Morgan Kaufmann, San Mateo, pp 733–739
Cummins R, O’Riordan C (2006a) An analysis of the solution space for genetically programmed term-weighting schemes in information retrieval. In: Bell DA (ed) AICS 2006, Belfast
Cummins R, O’Riordan C (2006b) Evolving local and global weighting schemes in information retrieval. Inf Retr 9:311–330
Cummins R, O’Riordan C (2006c) Term-weighting in information retrieval using genetic programming: a three stage process. In: Brewka G et al (eds) The 17th European conference on artificial intelligence, Riva del Garda. IOS, Amsterdam, pp 793–794
Dignum S, Poli R (2007) Generalisation of the limiting distribution of program sizes in tree-based genetic programming and analysis of its effects on bloat. In: Thierens D et al (eds) GECCO 2007, London, vol 2. ACM, New York, pp 1588–1595
Dracopoulos DC (1997) Evolutionary learning algorithms for neural adaptive control. Perspectives in neural computing. Springer, Berlin
EC-Digest (1985–2013). Available from http://ec-digest.research.ucf.edu/
Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin
El-Bakry SY, Radi A (2006) Genetic programming approach for electron–alkali–metal atom collisions. Int J Mod Phys B 20:5463–5471
El-Bakry MY, Radi A (2007) Genetic programming approach for flow of steady state fluid between two eccentric spheres. Appl Rheol 17:68210
Foster JA (2001) Review: discipulus: a commercial genetic programming system. Genet Program Evol Mach 2:201–203
Fraser A, Weinbrenner T (1993–1997) GPC++ genetic programming C++ class library. Available from http://www0.cs.ucl.ac.uk/staff/ucacbbl/ftp/weinbenner/gp.html
Fukunaga A (2002) Automated discovery of composite SAT variable selection heuristics. In: Proceedings of the national conference on artificial intelligence, Edmonton, pp 641–648
Fukunaga AS (2004) Evolving local search heuristics for SAT using genetic programming. In: Deb K et al (eds) GECCO 2004, Seattle. LNCS 3103. Springer, Berlin, pp 483–494
Gagné C, Parizeau M (2002) BEAGLE: a new C++ evolutionary computation framework. In: Langdon WB et al (eds) Proceedings of the GECCO. Morgan Kaufmann, San Mateo, New York, p 888
Genetic Programming Mailing List (2001–2013). Available at http://tech.groups.yahoo.com/group/genetic_programming/
Gruau F (1994a) Neural network synthesis using cellular encoding and the genetic algorithm. PhD thesis, Laboratoire de l’Informatique du Parallilisme, Ecole Normale Superieure de Lyon
Gruau F (1994b) Genetic micro programming of neural networks. In: Kinnear KE Jr (ed) Advances in genetic programming, ch 24. MIT, Cambridge, pp 495–518
Gruau F (1996) On using syntactic constraints with genetic programming. In: Angeline PJ, Kinnear KE Jr (eds) Advances in genetic programming 2, ch 19. MIT, Cambridge, pp 377–394
Gruau F, Whitley D (1993) Adding learning to the cellular development process: a comparative study. Evol Comput 1:213–233
Hauptman A, Sipper M (2005) GP-endchess: using genetic programming to evolve chess endgame players. In: Keijzer M et al (eds) Proceedings of the 8th European conference on genetic programming, Lausanne. LNCS 3447. Springer, Berlin, pp 120–131
Hauptman A, Sipper M (2007) Evolution of an efficient search algorithm for the mate-in-N problem in chess. In: Ebner M et al (eds) Proceedings of the 10th European conference on genetic programming, Valencia. LNCS 4445. Springer, Berlin, pp 78–89
Hauptman A, Elyasaf A, Sipper M, Karmon A (2009) GP-rush: using genetic programming to evolve solvers for the rush hour puzzle. In: Raidl G et al (eds) GECCO 2009, Montreal. ACM, New York, pp 955–962
Haynes TD, Schoenefeld DA, Wainwright RL (1996) Type inheritance in strongly typed genetic programming. In: Angeline PJ, Kinnear KE Jr (eds) Advances in genetic programming 2, ch 18. MIT, Cambridge, pp 359–376
Hoai NX, McKay RI, Abbass HA (2003) Tree adjoining grammars, language bias, and genetic programming. In: Ryan C et al (eds) Proceedings of the EuroGP 2003, Essex. LNCS 2610. Springer, Berlin, pp 335–344
Hoang T-H, Essam D, McKay RI, Nguyen XH (2007) Building on success in genetic programming: adaptive variation and developmental evaluation. In: Proceedings of the 2007 international symposium on intelligent computation and applications, Wuhan. China University of Geosciences Press
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT, Cambridge. First published by University of Michigan Press 1975
Howard D, Kolibal K (2005) Solution of differential equations with genetic programming and the stochastic Bernstein interpolation. Technical report BDS-TR-2005-001, University of Limerick
Hu J, Goodman ED, Li S, Rosenberg R (2008) Automated synthesis of mechanical vibration absorbers using genetic programming. Artif Intell Eng Des Anal Manuf 22:207–217
Iba H (1996) Genetic programming. Tokyo Denki University Press, Tokyo
Jacob C (1997) Principia Evolvica—Simulierte Evolution mit Mathematica. dpunkt.verlag, Heidelberg
Jacob C (2001) Illustrating evolutionary computation with mathematica. Morgan Kaufmann, San Mateo
Keane MA, Koza JR, Streeter MJ (2005) Human-competitive automated engineering design and optimization by means of genetic programming. In: Periaux J et al (eds) Evolutionary algorithms and intelligent tools in engineering optimization. WIT, Southampton
Keijzer M, Baptist M, Babovic V, Uthurburu JR (2005) Determining equations for vegetation induced resistance using genetic programming. In: Beyer H-G et al (eds) GECCO 2005, Washington, DC, vol 2. ACM, New York, pp 1999–2006
Khosraviani B, Levitt RE, Koza JR (2004) Organization design optimization using genetic programming. In: Keijzer M (ed) Late breaking papers at GECCO 2004, Seattle
Kinnear KE Jr (1993) Evolving a sort: lessons in genetic programming. In: Proceedings of the 1993 international conference on neural networks, vol 2. IEEE, Piscataway, San Francisco, CA, pp 881–888
Kinnear KE Jr (ed) (1994a) Advances in genetic programming. MIT, Cambridge
Kinnear KE Jr (1994b) Fitness landscapes and difficulty in genetic programming. In: Proceedings of the 1994 IEEE world conference on computational intelligence, Orlando, vol 1. IEEE, Piscataway, pp 142–147
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT, Cambridge
Koza JR (1994) Genetic programming II: automatic discovery of reusable programs. MIT, Cambridge
Koza JR (1995) Two ways of discovering the size and shape of a computer program to solve a problem. In: Eshelman L (ed) Proceedings of the 6th international conference on genetic algorithms, Pittsburgh. Morgan Kaufmann, San Mateo, pp 287–294
Koza JR, Andre D, Bennett FH III, Keane MA (1996a) Use of automatically defined functions and architecture-altering operations in automated circuit synthesis using genetic programming. In: Koza JR et al (eds) Proceedings of the 1st annual conference on genetic programming 1996, Stanford. MIT, Cambridge, pp 132–149
Koza JR, Bennett FH III, Andre D, Keane MA (1996b) Automated WYWIWYG design of both the topology and component values of electrical circuits using genetic programming. In: Koza JR et al (eds) Proceedings of the 1st annual conference on genetic programming 1996, Stanford. MIT, Cambridge, pp 123–131
Koza JR, Bennett FH III, Andre D, Keane MA (1999a) The design of analog circuits by means of genetic programming. In: Bentley P (ed) Evolutionary design by computers, ch 16. Morgan Kaufmann, San Francisco, pp 365–385
Koza JR, Andre D, Bennett FH III, Keane MA (1999b) Genetic programming 3: Darwinian invention and problem solving. Morgan Kaufman, San Mateo
Koza JR, Keane MA, Streeter MJ, Mydlowec W, Yu J, Lanza G (2003) Genetic programming IV: routine human-competitive machine intelligence. Kluwer, Dordrecht
Koza JR, Al-Sakran SH, Jones LW (2005) Automated re-invention of six patented optical lens systems using genetic programming. In: Beyer H-G et al (eds) GECCO 2005, Washington, DC, vol 2. ACM, New York, pp 1953–1960
Koza JR, Al-Sakran SH, Jones LW (2008) Automated ab initio synthesis of complete designs of four patented optical lens systems by means of genetic programming. Artif Intell Eng Des Anal Manuf 22:249–273
Krawiec K (2004) Evolutionary feature programming: cooperative learning for knowledge discovery and computer vision, vol 385. Wydawnictwo Politechniki Poznanskiej, Poznan
Lam B, Ciesielski V (2004) Discovery of human-competitive image texture feature extraction programs using genetic programming. In: Deb K et al (eds) GECCO 2004, Seattle. LNCS 3103. Springer, Berlin, pp 1114–1125
Langdon WB (1998a) The evolution of size in variable length representations. In: IEEE international conference on evolutionary computation, Anchorage. IEEE, Piscataway, pp 633–638
Langdon WB (1998b) Genetic programming and data structures: genetic programming + data structures = automatic programming! Genetic programming, vol 1. Kluwer, Boston
Langdon WB (2000) Size fair and homologous tree genetic programming crossovers. Genet Program Evol Mach 1:95–119
Langdon WB (2002) Convergence rates for the distribution of program outputs. In: Langdon WB et al (eds) GECCO 2002, New York. Morgan Kaufmann, San Mateo, pp 812–819
Langdon WB (2003a) How many good programs are there? How long are they? In: De Jong KA et al (eds) Foundations of genetic algorithms VII. Morgan Kaufmann, San Mateo, pp 183–202
Langdon WB (2003b) Convergence of program fitness landscapes. In: Cantú-Paz E et al (eds) GECCO 2003, Chicago. LNCS 2724. Springer, Berlin, pp 1702–1714
Langdon WB (2003c) The distribution of reversible functions is normal. In: Riolo RL, Worzel B (eds) Genetic programming theory and practise, ch 11. Kluwer, Dordrecht, pp 173–188
Langdon WB (2005) The distribution of amorphous computer outputs. In: Stepney S, Emmott S (eds) The grand challenge in non-classical computation: international workshop, York
Langdon WB, Poli R (2002) Foundations of genetic programming. Springer, Berlin
Langdon WB, Poli R (2006) The halting probability in von Neumann architectures. In: Collet P, Tomassini M, Ebner M et al (eds) Proceedings of the 9th European conference on genetic programming, Budapest. LNCS 3905. Springer, Berlin, pp 225–237
Langdon WB, Gustafson SM, Koza J (1995–2012) The genetic programming bibliography. Available at http://www.cs.bham.ac.uk/~wbl/biblio/
Langdon WB, Soule T, Poli R, Foster JA (1999) The evolution of size and shape. In: Spector L et al (eds) Advances in genetic programming 3, ch 8. MIT, Cambridge, pp 163–190
Larrañaga P, Lozano JA (2002) Estimation of distribution algorithms, a new tool for evolutionary computation. Kluwer, Dordrecht
Lindenmayer A (1968) Mathematic models for cellular interaction in development I and II. J Theor Biol 18:280–299, 300–315
Lipson H (2004) How to draw a straight line using a GP: benchmarking evolutionary design against 19th century kinematic synthesis. In: Keijzer M (ed) Late breaking papers at GECCO 2004, Seattle
Lohn J, Hornby G, Linden D (2004) Evolutionary antenna design for a NASA spacecraft. In: O’Reilly U-M et al (eds) Genetic programming theory and practice II, ch 18. Springer, Berlin, pp 301–315
Looks M (2007) Scalable estimation-of-distribution program evolution. In: Lipson H (ed) GECCO 2007, London. ACM, New York, pp 539–546
Looks M, Goertzel B, Pennachin C (2005) Learning computer programs with the Bayesian optimization algorithm. In: Beyer H-G et al (eds) GECCO 2005, Washington, DC, vol 1. ACM, New York, pp 747–748
Luke S (1998) Genetic programming produced competitive soccer softbot teams for robocup97. In: Koza JR, Banzhaf W, Chellapilla K et al (eds) Proceedings of the 3rd annual conference on genetic programming 1998, Madison. Morgan Kaufmann, San Mateo, pp 214–222
Luke S, Panait L, Balan G et al (2000–2013) ECJ: a java-based evolutionary computation research system. Available at http://cs.gmu.edu/~eclab/projects/ecj/
Massey P, Clark JA, Stepney S (2005) Evolution of a human-competitive quantum Fourier transform algorithm using genetic programming. In: Beyer H-G et al (eds) GECCO 2005, Washington, DC, vol 2. ACM, New York, pp 1657–1663
Mitavskiy B, Rowe J (2006) Some results about the Markov chains associated to GPs and to general EAs. Theor Comput Sci 361:72–110
Montana DJ (1995) Strongly typed genetic programming. Evol Comput 3:199–230
Nguyen TV, Weimer W, Le Goues C, Forrest S (2009) Using execution paths to evolve software patches. In: McMinn P, Feldt R (eds) International conference on software testing, verification and validation workshops, Denver, pp 152–153
Nikolaev N, Iba H (2006) Adaptive learning of polynomial networks genetic programming, backpropagation and Bayesian methods. Genetic and evolutionary computation, vol 4. Springer, Berlin
Nordin P (1997) Evolutionary program induction of binary machine code and its applications. PhD thesis, der Universitat Dortmund am Fachereich Informatik
Nordin P, Johanna W (2003) Humanoider: Sjavlarande robotar och artificiell intelligens. Liber, Stockholm
Olsson JR (1994) Inductive functional programming using incremental program transformation and execution of logic programs by iterative-deepening A* SLD-tree search. PhD thesis, University of Oslo
O’Neill M, Ryan C (2003) Grammatical evolution: evolutionary automatic programming in a arbitrary language. Genetic programming, vol 4. Kluwer, Dordrecht
Perez CB, Olague G (2008) Learning invariant region descriptor operators with genetic programming and the F-measure. In: 19th international conference on pattern recognition, Tampa, pp 1–4
Perez CB, Olague G (2009) Evolutionary learning of local descriptor operators for object recognition. In: Raidl G et al (eds) GECCO 2009, Montreal. ACM, New York, pp 1051–1058
Poli R (2000a) Hyperschema theory for GP with one-point crossover, building blocks, and some new results in GA theory. In: Poli R et al (eds) Proceecings of the EuroGP 2000 on genetic programming, Tübingen. LNCS 1802. Springer, Berlin, pp 163–180
Poli R (2000b) Exact schema theorem and effective fitness for GP with one-point crossover. In: Whitley D et al (eds) GECCO 2000, Las Vegas. Morgan Kaufmann, San Mateo, pp 469–476
Poli R (2001a) Exact schema theory for genetic programming and variable-length genetic algorithms with one-point crossover. Genet Program Evol Mach 2:123–163
Poli R (2001b) General schema theory for genetic programming with subtree-swapping crossover. In: Proceedings of the EuroGP 2001 on genetic programming, Como. LNCS 2038. Springer, Berlin
Poli R (2003) A simple but theoretically-motivated method to control bloat in genetic programming. In: Ryan C et al (eds) Proceedings of the EuroGP 2003 on genetic programming, Essex. LNCS 3003. Springer, Berlin, pp 211–223
Poli R, Langdon WB (2006) Efficient Markov chain model of machine code program execution and halting. In: Riolo RL et al (eds) Genetic programming theory and practice IV. Genetic and evolutionary computation, vol 5, ch 13. Springer, Berlin
Poli R, McPhee NF (2003a) General schema theory for genetic programming with subtree-swapping crossover: I. Evol Comput 11:53–66
Poli R, McPhee NF (2003b) General schema theory for genetic programming with subtree-swapping crossover: II. Evol Comput 11:169–206
Poli R, McPhee NF (2008a) Covariant parsimony pressure in genetic programming. Technical report CES-480, University of Essex
Poli R, McPhee NF (2008b) A linear estimation-of-distribution GP system. In: O’Neill M et al (eds) Proceedings of the EuroGP 2008, Naples. LNCS 4971. Springer, Berlin, pp 206–217
Poli R, Rowe JE, McPhee NF (2001) Markov chain models for GP and variable-length GAs with homologous crossover. In: Spector L et al (eds) GECCO 2001, San Francisco. Morgan Kaufmann, San Mateo, pp 112–119
Poli R, McPhee NF, Rowe JE (2004) Exact schema theory and markov chain models for genetic programming and variable-length genetic algorithms with homologous crossover. Genet Program Evol Mach 5:31–70
Poli R, Langdon WB, Dignum S (2007) On the limiting distribution of program sizes in tree-based genetic programming. In: Ebner M et al (eds) Proceedings of the 10th European conference on genetic programming, Valencia. LNCS 4445. Springer, Berlin, pp 193–204
Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. Published via http://lulu.com and http://www.gp-field-guide.org.uk (with contributions by J. R. Koza)
Poli R, Vanneschi L, Langdon WB, McPhee NF (2010) Theoretical results in genetic programming: the next ten years? Genet Program Evol Mach 11:285–320. 10th anniversary issue: progress in genetic programming and evolvable machines
Punch B, Zongker D (1998) lil-gp genetic programming system. Available at http://garage.cse.msu.edu/software/lil-gp/
Radi A (2007) Prediction of non-linear system in optics using genetic programming. Int J Mod Phys C 18:369–374
Radi AM, El-Bakry SY (2007) Genetic programming approach for positron collisions with alkali-metal atom. In: Thierens D et al (eds) GECCO 2007, London, vol 2. ACM, New York, pp 1756–1756
Raja A, Atif Azad RM, Flanagan C, Ryan C (2007) Real-time, non-intrusive evaluation of voIP. In: Ebner M et al (eds) Proceedings of the 10th European conference on genetic programming, Valencia. LNCS 4445. Springer, Berlin, pp 217–228
Ratle A, Sebag M (2001) Avoiding the bloat with probabilistic grammar-guided genetic programming. In: Collet P et al (eds) Artificial evolution 5th international conference on evolution artificielle, EA, Le Creusot. LNCS 2310. Springer, Berlin, pp 255–266
Riolo RL, Worzel B (eds) (2003) Genetic programming theory and practice. Genetic programming, vol 6. Kluwer, Boston
RML Technologies (1998–2011) Discipulus genetic programming software. Available from http://www.rmltech.com/
Rosca J (2003) A probabilistic model of size drift. In: Riolo RL, Worzel B (eds) Genetic programming theory and practice, ch 8. Kluwer, Dordrecht, pp 119–136
Rosca JP, Ballard DH (1996) Discovery of subroutines in genetic programming. In: Angeline PJ, Kinnear KE Jr (eds) Advances in genetic programming 2, ch 9. MIT, Cambridge, pp 177–202
Rothlauf F (2006) Representations for genetic and evolutionary algorithms, 2nd edn. Springer, Berlin. First published 2002, 2nd edn available electronically
Ryan C (1999) Automatic re-engineering of software using genetic programming. Genetic programming, vol 2. Kluwer, Dordrecht
Ryan C, Collins JJ, O’Neill M (1998) Grammatical evolution: evolving programs for an arbitrary language. In: Banzhaf W et al (eds) Proceedings of the 1st European workshop on genetic programming, Paris. LNCS 1391. Springer, Berlin, pp 83–95
Salustowicz RP, Schmidhuber J (1997) Probabilistic incremental program evolution. Evol Comput 5:123–141
Schmidt M, Lipson H (2009a) Distilling free-form natural laws from experimental data. Science 324:81–85
Schmidt MD, Lipson H (2009b) Solving iterated functions using genetic programming. In: Esparcia AI et al (eds) GECCO 2009 late-breaking papers, Montreal. ACM, New York, pp 2149–2154
Sekanina L (2003) Evolvable components: from theory to hardware implementations. Natural computing. Springer, Berlin
Shan Y, Abbass H, McKay RI, Essam D (2002) AntTAG: a further study. In: Sarker R, McKay B (eds) Proceedings of the 6th Australia–Japan joint workshop on intelligent and evolutionary systems, Canberra
Shan Y, McKay RI, Abbass HA, Essam D (2003) Program evolution with explicit learning: a new framework for program automatic synthesis. In: Sarker R et al (eds) Proceedings of the CEC 2003, Canberra. IEEE, Piscataway, pp 1639–1646
Shan Y, McKay RI, Essam D, Abbass HA (2006) A survey of probabilistic model building genetic programming. In: Pelikan M et al (eds) Scalable optimization via probabilistic modeling: from algorithms to applications. Studies in computational intelligence, vol 33, ch 6. Springer, Berlin, pp 121–160
Shichel Y, Ziserman E, Sipper M (2005) GP-robocode: using genetic programming to evolve robocode players. In: Keijzer M et al (eds) Proceedings of the 8th European conference on genetic programming, Lausanne. LNCS 3447. Springer, Berlin, pp 143–154
Sipper M (2006) Attaining human-competitive game playing with genetic programming. In: El Yacoubi S et al (eds) Proceedings of the 7th international conference on cellular automata, for research and industry, Perpignan. LNCS 4173. Springer, Berlin, p 13. (invited lectures)
Soule T, Foster JA (1998) Effects of code growth and parsimony pressure on populations in genetic programming. Evol Comput 6:293–309
Spector L (2004) Automatic quantum computer programming: a genetic programming approach. Genetic programming, vol 7. Kluwer, Boston
Spector L, Bernstein HJ (2003) Communication capacities of some quantum gates, discovered in part through genetic programming. In: Shapiro JH, Hirota O (eds) Proceedings of the 6th international conference on quantum communication, measurement, and computing, Cambridge. Rinton, Princeton, pp 500–503
Spector L, Klein J (2008) Machine invention of quantum computing circuits by means of genetic programming. Artif Intell Eng Des Anal Manuf 22:275–283
Spector L, Barnum H, Bernstein HJ (1998) Genetic programming for quantum computers. In: Koza JR et al (eds) Proceedings of the 3rd annual conference on genetic programming 1998, Madison. Morgan Kaufmann, San Mateo, pp 365–373
Spector L, Barnum H, Bernstein HJ, Swamy N (1999a) Finding a better-than-classical quantum AND/OR algorithm using genetic programming. In: Angeline PJ et al (eds) Proceedings of the CEC 1999, Washington, DC, vol 3. IEEE, Piscataway, pp 2239–2246
Spector L, Barnum H, Bernstein HJ, Swamy N (1999b) Quantum computing applications of genetic programming. In: Spector L et al (eds) Advances in genetic programming 3, ch 7. MIT, Cambridge, pp 135–160
Spector L, Langdon WB, O’Reilly UM, Angeline PJ (eds) (1999c) Advances in genetic programming 3. MIT, Cambridge
Spector L, Clark DM, Lindsay I, Barr B, Klein J (2008) Genetic programming for finite algebras. In: Keijzer M et al (eds) GECCO 2008, Atlanta. ACM, New York, pp 1291–1298
Stadelhofer R, Banzhaf W, Suter D (2008) Evolving blackbox quantum algorithms using genetic programming. Artif Intell Eng Des Anal Manuf 22:285–297
Stephens CR, Waelbroeck H (1997) Effective degrees of freedom in genetic algorithms and the block hypothesis. In: Bäck T (ed) Proceedings of the 7th international conference on genetic algorithms, East Lansing. Morgan Kaufmann, San Mateo, pp 34–40
Stephens CR, Waelbroeck H (1999) Schemata evolution and building blocks. Evol Comput 7:109–124
Tay JC, Ho NB (2008) Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Comput Ind Eng 54:453–473
Trujillo L, Olague G (2006a) Using evolution to learn how to perform interest point detection. In: Tang XY et al (ed) ICPR 2006, Hong Kong, vol 1, pp 211–214
Trujillo L, Olague G (2006b) Synthesis of interest point detectors through genetic programming. In: Keijzer M et al (eds) GECCO 2006, Seattle, vol 1. ACM, New York, pp 887–894
Tsang E, Jin N (2006) Incentive method to handle constraints in evolutionary. In: Collet P et al (eds) Proceedings of the 9th European conference on genetic programming, Budapest. LNCS 3905. Springer, Berlin, pp 133–144
Tsang EPK, Li J (2002) EDDIE for financial forecasting. In: Chen S-H (ed) Genetic algorithms and genetic programming in computational finance, ch 7. Kluwer, Dordrecht, pp 161–174
Turing AM (1948) Intelligent machinery. National Physical Laboratory Report. Reprinted in Ince DC (ed) (1992) Mechanical intelligence: collected works of A. M. Turing, pp 107–127. North-Holland, Amsterdam. Also reprinted in Meltzer B, Michie D (eds) (1969) Machine intelligence 5. Edinburgh University Press
Turing AM (1950) Computing machinery and intelligence. Mind 49:433–460
Weimer W, Nguyen T, Le Goues C, Forrest S (2009) Automatically finding patches using genetic programming. In: Fickas S (ed) International conference on software engineering, Vancouver, pp 364–374
Whigham PA (1996) Search bias, language bias, and genetic programming. In: Koza JR et al (eds) Proceedings of the 1st annual conference on genetic programming 1996, Stanford. MIT, Cambridge, pp 230–237
Whitley LD (1994) A genetic algorithm tutorial. Stat Comput 4:65–85
Wong ML, Leung KS (1996) Evolving recursive functions for the even-parity problem using genetic programming. In: Angeline PJ, Kinnear KE Jr (eds) Advances in genetic programming 2, ch 11. MIT, Cambridge, pp 221–240
Wong ML, Leung KS (2000) Data mining using grammar based genetic programming and applications. Genetic programming, vol 3. Kluwer, Dordrecht
Yanai K, Iba H (2003) Estimation of distribution programming based on bayesian network. In: Sarker R et al (eds) Proceedings of the CEC 2003, Canberra. IEEE, Piscataway, pp 1618–1625
Yu T (2001) Hierachical processing for evolving recursive and modular programs using higher order functions and lambda abstractions. Genet Program Evol Mach 2:345–380
Zhang B-T, Mühlenbein H (1993) Evolving optimal neural networks using genetic algorithms with Occam’s razor. Complex Syst 7:199–220
Zhang B-T, Mühlenbein H (1995) Balancing accuracy and parsimony in genetic programming. Evol Comput 3:17–38
Zhang B-T, Ohm P, Mühlenbein H (1997) Evolutionary induction of sparse neural trees. Evol Comput 5:213–236
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Poli, R., Koza, J. (2014). Genetic Programming. In: Burke, E., Kendall, G. (eds) Search Methodologies. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-6940-7_6
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