By Malcolm I. Heywood, James McDermott, Mauro Castelli, Ernesto Costa, Kevin Sim

This e-book constitutes the refereed court cases of the nineteenth eu convention on Genetic Programming, EuroGP 2016, held in Porto, Portugal, in March/April 2016 co-located with the Evo*2016 occasions: EvoCOP, EvoMUSART, and EvoApplications.

The eleven revised complete papers awarded including eight poster papers have been conscientiously reviewed and chosen from 36 submissions. the wide variety of issues during this quantity displays the present nation of analysis within the box. therefore, we see issues as different as semantic tools, recursive courses, grammatical equipment, coevolution, Cartesian GP, characteristic choice, metaheuristics, evolvability, and health predictors; and purposes together with photo processing, one-class class, SQL injection assaults, numerical modelling, streaming information class, production and optimisation of circuits, multi-class category, scheduling in production and instant networks.

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Additional resources for Genetic Programming: 19th European Conference, EuroGP 2016, Porto, Portugal, March 30 - April 1, 2016, Proceedings

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Appl. 1(1), 63–73 (2007) 23. : Formal verification of candidate solutions for postsynthesis evolutionary optimization in evolvable hardware. Genet. Program Evolvable Mach. 12(3), 305–327 (2011) 34 P. Dvoracek and L. Sekanina 24. : Circuit approximation using single- and multi-objective cartesian GP. , et al. ) EuroGP. LNCS, vol. 9025, pp. 217–229. Springer International Publishing, Switzerland (2015) 25. : Evolutionary approach to approximate digital circuits design. IEEE Trans. Evol. Comput. 19(3), 432–444 (2015) 26.

Approximate arithmetic for low-power image median filtering. Circuits Syst. Signal Process. 34(10), 3191–3219 (2015) 14. : Abacus: a technique for automated behavioral synthesis of approximate computing circuits. In: Proceedings of the Conference on Design, Automation and Test in Europe, DATE 2014, pp. 1–6. EDA Consortium (2014) 15. : Evolving cellular automata for detecting edges in hyperspectral images. In: 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2012) 16.

The specific formulation is presented in Sect. 2. Finally, Sect. 3 establishes a common parameterization for use throughout the study. , C]. Given the later benchmarking parameterization this corresponds to no more than 25 generations. Previous studies had compared StreamGP under the uniform sampling protocol to non-evolutionary streaming algorithms [16, 17]. 42 S. Khanchi et al. 5 The Shift dataset [20] defines a 5-class task in 6dimensions in which two decision trees are used to define rules for two separate 5-class classification tasks: C1 and C2.

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