By Piero P. Bonissone (auth.), Jing Liu, Cesare Alippi, Bernadette Bouchon-Meunier, Garrison W. Greenwood, Hussein A. Abbass (eds.)
This state of the art survey bargains a renewed and clean specialize in the development in evolutionary computation, in neural networks, and in fuzzy structures. The ebook offers the services and studies of major researchers spanning a various spectrum of computational intelligence in those components. the result's a balanced contribution to the examine quarter of computational intelligence that are meant to serve the neighborhood not just as a survey and a reference, but in addition as an idea for the longer term development of the state-of-the-art of the sector. The thirteen chosen chapters originate from lectures and shows given on the IEEE international Congress on Computational Intelligence, WCCI 2012, held in Brisbane, Australia, in June 2012.
Read or Download Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures PDF
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Extra info for Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures
In the third experiment, ESL was compared to the traditional student-teacher model of social learning. Figure 4 shows the results of applying a monocultural egalitarian social learning algorithm to the foraging domain in both the Lamarckian and Darwinian paradigms. The performance of both algorithms quickly converges, with Lamarckian reaching a higher-ﬁtness solution than Darwinian evolution. In the context of on-line evolutionary learning algorithms, previous work  showed that Darwinian evolution is likely to be preferable to Lamarckian evolution in dynamic environments where adaptation is essential and the Baldwin eﬀect  may be advantageous.
4 Social Learning Results Three experiments were performed: ESL was ﬁrst applied to the entire population (without subcultures), and the best way to make use of learning (Darwinian vs. Lamarckian) determined. The eﬀect of maintaining diversity through explicit subcultures was then evaluated. In the third experiment, ESL was compared to the traditional student-teacher model of social learning. Figure 4 shows the results of applying a monocultural egalitarian social learning algorithm to the foraging domain in both the Lamarckian and Darwinian paradigms.
Active guidance for a ﬁnless rocket using neuroevolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 2084–2095. Morgan Kaufmann, San Francisco (2003) 17. : Social learning in population-based adaptive systems. In: IEEE Congress on Evolutionary Computation, CEC 2008 (IEEE World Congress on Computational Intelligence), pp. 1386–1392. IEEE (2008) 18. : Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis. Science 317(5843), 1360 (2007) 19.
Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures by Piero P. Bonissone (auth.), Jing Liu, Cesare Alippi, Bernadette Bouchon-Meunier, Garrison W. Greenwood, Hussein A. Abbass (eds.)