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Extra resources for Hackable Magazine [FR], Issue 2 (September, October 2014)
The terminal set consists of random numbers, and of the function variables. Standard GP parameters are used, as shown in Table 2. The fitness function is the Mean Squared Error (MSE). 2 Accuracy Evaluation In order to compare the accuracy of standard GP and SGP, we run GP and SGP for 200 generations using a population of 100 individuals on the above described benchmarks. Figure 2a shows the result of the comparison. According to the figure, SGP performs better than the standard GP in terms of accuracy.
This is probably due, in accordance with the results found in the chapter of Burke, to the presence of many local optima. In the case of genotypic diversity (see Fig. 4b) in SGP, in very early generations the correlation between the fitness and genotypic entropy is positive. After these early generations, the correlation becomes lower and close to zero and afterward becomes negative. , a similar behavior have been evidenced. 5 −1 0 50 100 150 200 Generation (b) Fig. 4 a Correlation between fitness and phenotypic diversity, b Correlation between fitness and genotypic diversity Statistical Genetic Programming: The Role of Diversity 47 Our investigation results are similar to those of .
In: Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications, pp. 25–42 (2001) 19. Michal Wozniak. Experiments with trained and untrained fusers. In Emilio Corchado, Juan Corchado, and Ajith Abraham, editors, Innovations in Hybrid Intelligent Systems, volume 44 of Advances in Soft Computing, pages 144–150. Springer Berlin / Heidelberg, 2007. 20. Wozniak, Michal, Zmyslony, Marcin: Combining classifiers using trained fuser—analytical and experimental results. Neural Netw.