By Clara Pizzuti, Giandomenico Spezzano
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, along side the twenty fourth Italian Workshop on Neural Networks, WIRN 2014.
The sixteen papers provided 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 tools resembling 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
In particular, GKA (Genetic K-means algorithm), FGKA (Fast GKA) and IGKA have proven to be able to ﬁnd a global optimal partition given a preﬁxed number of clusters  while Bandyopadhyay and Maulik  have used GA to discover automatically also the number of clusters. Clustering, as stated above, is an unsupervised technique that partitions data in order to minimize variance within groups and maximizing variance between groups. This statistical principle is well grounded on the information contained in the data but completely ungrounded on the needs and on the perceptual ability of who have to make use of such partition.
Neural Netw. 23(1), 89–107 (2010) 6. : Cluster Analysis. A Hodder Arnold Publication. Wiley, New York (2001) 7. : A History of Modern Psychology. Wiley, New York (2002) 8. : A survey of evolutionary algorithms for clustering. Trans. Syst. Man Cybern. Part C 39(2), 133–155 (2009) 9. : The cognitive unconscious. 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.
The study, which involves diﬀerent structures of the problem, shows that Q-PSO outperforms PSO in almost all the parameter conﬁgurations that we considered. We observe in fact that Q-PSO achieves very good results in ﬁnding minimum values and averages of minimum values over a set of runs and in few generations of the algorithm. In conclusion, in this study we introduced a new, easy to implement and eﬀective approach that allows considering qualitative variables in complex optimization problems.