A study on Koza’s performance measures

Author:

Barrero David F.,Castaño Bonifacio,R-Moreno María D.,Camacho David

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Hardware and Architecture,Theoretical Computer Science,Software

Reference28 articles.

1. A. Agresti, B.A. Coull, Approximate is better than ’exact’ for interval estimation of binomial proportions. Am. Stat. 52, 119–126 (1998)

2. P.J. Angeline, An investigation into the sensitivity of genetic programming to the frequency of leaf selection during subtree crossover. in Proceedings of the First Annual Conference on Genetic Programming (GECCO 1996). (MIT Press, Cambridge, MA, 1996), pp. 21–29

3. D.F. Barrero, D. Camacho, M.D. R-Moreno, Confidence intervals of success rates in evolutionary computation. in Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO 2010). (ACM, Portland, Oregon, 2010), pp. 975–976. doi: 10.1145/1830483.1830657

4. D.F. Barrero, B. Castaño, M.D. R-Moreno, D. Camacho, Statistical Distribution of Generation-to-Success in GP: Application to Model Accumulated Success Probability, in Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011, LNCS, vol. 6621, ed. by S. Silva, J.A. Foster, M. Nicolau, M. Giacobini, P. Machado (Springer, Turin, 2011), pp. 155–166

5. D.F. Barrero, M.D. R-Moreno, B. Castano, D. Camacho, An empirical study on the accuracy of computational effort in genetic programming, in Proceedings of the 2011 IEEE Congress on Evolutionary Computation. IEEE Computational Intelligence Society, ed. by A.E. Smith (IEEE Press, New Orleans, 2011), pp. 1169–1176

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1. Improving experimental methods on success rates in evolutionary computation;Journal of Experimental & Theoretical Artificial Intelligence;2016-08-03

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