Abstract
Motivated by biological inspiration and the issue of code disruption, we develop a new form of LGP called Parallel LGP (PLGP). PLGP programs consist of n lists of instructions. These lists are executed in parallel, after which the resulting vectors are combined to produce program output. PGLP limits the disruptive effects of crossover and mutation, which allows PLGP to significantly outperform regular LGP.
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References
Andre, D., Koza, J.R.: A parallel implementation of genetic programming that achieves super-linear performance. Information Sciences 106(3-4), 201–218 (1998)
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction on the Automatic Evolution of computer programs and its Applications. Morgan Kaufmann Publishers, Dpunkt-Verlag, San Francisco, Heidelburg (1998)
Brameier, M., Banzhaf, W.: Linear Genetic Programming. Genetic and Evolutionary Computation, vol. XVI. Springer, Heidelberg (2007)
Fogelberg, C., Zhang, M.: Linear genetic programming for multi-class object classification. In: Zhang, S., Jarvis, R.A. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 369–379. Springer, Heidelberg (2005)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, MIT Press, Ann Arbor, Cambridge (1975)
Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)
Krawiec, K., Bhanu, B.: Visual learning by evolutionary and coevolutionary feature synthesis. IEEE Transactions on Evolutionary Computation 11(5), 635–650 (2007)
Olague Caballero, G., Romero, E., Trujillo, L., Bhanu, B.: Multiclass object recognition based on texture linear genetic programming. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 291–300. Springer, Heidelberg (2007)
Olaguea, G., Cagnoni, S., Lutton, E. (eds.): special issue on evolutionary computer vision and image understanding. Pattern Recognition Letters 27(11) (2006)
Hettich, S., Blake, C., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Zhang, M., Ciesielski, V.B., Andreae, P.: A domain-independent window approach to multiclass object detection using genetic programming. EURASIP Journal on Applied Signal Processing 2003(8), 841–859 (2003); special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis
Zhang, M., Gao, X., Lou, W.: A new crossover operator in genetic programming for object classification. IEEE Transactions on Systems, Man and Cybernetics, Part B 37(5), 1332–1343 (2007)
Zhang, M., Smart, W.: Using gaussian distribution to construct fitness functions in genetic programming for multiclass object classification. Pattern Recognition Letters 27(11), 1266–1274 (2006); evolutionary Computer Vision and Image Understanding
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Downey, C., Zhang, M. (2011). Parallel Linear Genetic Programming. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds) Genetic Programming. EuroGP 2011. Lecture Notes in Computer Science, vol 6621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20407-4_16
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DOI: https://doi.org/10.1007/978-3-642-20407-4_16
Publisher Name: Springer, Berlin, Heidelberg
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