Advances in Artificial Life and Evolutionary Computation: - download pdf or read online

By Clara Pizzuti, Giandomenico Spezzano

ISBN-10: 3319127446

ISBN-13: 9783319127446

ISBN-10: 3319127454

ISBN-13: 9783319127453

This e-book constitutes the revised chosen papers of the ninth Italian Workshop on Advances in man made lifestyles and Evolutionary Computation held in Vietri sul Mare, Italy, in might 2014, together with the twenty fourth Italian Workshop on Neural Networks, WIRN 2014.

The sixteen papers awarded were completely reviewed and chosen from forty submissions. They conceal the next subject matters: synthetic neural networks; fuzzy inference platforms; tough set; approximate reasoning; and optimization equipment corresponding to evolutionary computation, swarm intelligence, particle swarm optimization.

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Additional info for Advances in Artificial Life and Evolutionary Computation: 9th Italian Workshop, WIVACE 2014, Vietri sul Mare, Italy, May 14-15, Revised Selected Papers

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IEEE Trans. Evol. Comput. 6, 58–73 (2002) 6. : A novel set-based particle swarm optimization method for discrete optimization problem. IEEE Trans. Evol. Comput. 14(2), 278–300 (2010) 7. : A modied particle swarm optimizer, In: Proceedings of Evolutionary Computation, p. 6973 (1998) 8. : Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation 3 (1999) 9. : Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization.

Science 237(4821), 1445–1452 (1987) 10. : Sparse fitness evaluation for reducing user burden in interactive genetic algorithm. In: 1999 IEEE International Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE ’99, vol. 2, pp. 998–1003 (1999) 11. : R Fundamentals and Programming Techniques. Chapman & Hall/CRC, Boca Raton (2006) 12. : Density-based silhouette diagnostics for clustering methods. Stat. Comput. 21(3), 295–308 (2011) 13. : Breedbot: an evolutionary robotics application in digital content.

We address this problem by considering two different settings, which differ for the number of variables and complexity: – Case 1 : we characterize the problem with variables h, d and g, which requires to test 2 150 experimental points; – Case 2 : we characterize the problem with variables h, d, g, dL and dR, which requires to test 215 000 experimental points. The relative small number of experimental points of case 1 allows the evaluation of all the search space and the identification of the actual minimum value.

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Advances in Artificial Life and Evolutionary Computation: 9th Italian Workshop, WIVACE 2014, Vietri sul Mare, Italy, May 14-15, Revised Selected Papers by Clara Pizzuti, Giandomenico Spezzano


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