By Miguel Nicolau, Krzysztof Krawiec, Malcolm I. Heywood, Mauro Castelli, Pablo García-Sánchez, Juan J. Merelo, Victor Manuel Rivas Santos, Kevin Sim
The ebook constitutes the refereed lawsuits of the seventeenth ecu convention on Genetic Programming, Euro GP 2014, held in Grenada, Spain, in April 2014 co-located with the Evo*2014 occasions, Evo BIO, Evo COP, Evo MUSART and Evo Applications.
The 15 revised complete papers offered including five poster papers have been conscientiously reviewed and chosen shape forty submissions. the wide variety of subject matters during this quantity displays the present nation of analysis within the box. hence, we see themes as assorted as search-based software program engineering, snapshot research, dynamical structures, evolutionary robotics and operational learn to the rules of seek as characterised via semantic version operators.
Read or Download Genetic Programming: 17th European Conference, EuroGP 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers PDF
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Additional resources for Genetic Programming: 17th European Conference, EuroGP 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers
E. References 1. : Accelerating genetic programming through graphics processing units. In: Genetic Programming Theory and Practice VI. Genetic and Evolutionary Computation, pp. 1–19. Springer US (2009) 2. : The million song dataset. In: Proceedings of the 12th International Conference on Music Information Retrieval, ISMIR 2011 (2011) 3. : A data parallel approach to genetic programming using programmable graphics hardware. In: Proceedings of the 9th Annual GECCO Conference, GECCO 2007, pp. 1566–1573.
An example on how the values of the additional variables (right) can be easily produced starting from the original dataset (left). In this case, for each line, we computed the values of Δt and F to the next point, only. 4 ... 4 ... 9 ... 1 ... One of the known issues of symbolic regression and GP in general is the socalled overﬁtting: solutions that closely approximate training data often exploit exclusive features of the dataset, for example by including terms that model the noise as well. This leads to poor performances on validation sets.
In order to complement the information presented in Table 3, the number of times each algorithm presented the best performance was counted, the performance measure used was nMAE. This procedure showed that Simpliﬁcation GP presented the best performance in 5 of the 1,100 problems and GPPDE has the best performance in 1095 of the 1,100 problems. Figure 5 presents the average output of Simpliﬁcation GP and GPPDE on one of the ﬁve problems in which GPPDE did not exhibit the best performance. 0464, these two values were computed using nMAE.
Genetic Programming: 17th European Conference, EuroGP 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers by Miguel Nicolau, Krzysztof Krawiec, Malcolm I. Heywood, Mauro Castelli, Pablo García-Sánchez, Juan J. Merelo, Victor Manuel Rivas Santos, Kevin Sim