By Piero P. Bonissone (auth.), Jing Liu, Cesare Alippi, Bernadette Bouchon-Meunier, Garrison W. Greenwood, Hussein A. Abbass (eds.)
This cutting-edge survey bargains a renewed and clean specialize in the development in evolutionary computation, in neural networks, and in fuzzy structures. The publication provides the services and reports of top researchers spanning a various spectrum of computational intelligence in those components. the result's a balanced contribution to the learn quarter of computational intelligence that are meant to serve the group not just as a survey and a reference, but in addition as an concept for the long run development of the state-of-the-art of the sphere. 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.
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Additional info for Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures
And the results for each experiment are the average of 30 independent runs. The acceptability function for all experiments is to learn from any action yielding a positive reward. 3 The NEAT Neuroevolution Method NeuroEvolution of Augmenting Topologies (NEAT) is an evolutionary algorithm that generates recurrent neural networks. Through a process of adding and removing nodes and changing weights, NEAT evolves genomes that unfold into networks. In every generation, those networks with the highest ﬁtness reproduce, while those with the lowest ﬁtness are unlikely to do so.
4. The eﬀects of Darwinian and Lamarckian evolution when using a monocultural variant of ESL. While both evolutionary paradigms converge rapidly Lamarckian evolution is more eﬀective than Darwinian in the foraging domain. Consequently, Lamarckian evolution is the paradigm used in all remaining experiments. e. the rewards of each plant type are the same in every generation), in this experiment Lamarckian evolution outperforms Darwinian evolution. Nevertheless, in both cases performance converges to a lower score than that of simple neuroevolution.
It was therefore possible to characterize the most common strategies used in the tournament, as outlined below. The example teams and videos of games between then are available at the tournament website3 . Pack: The most prominent strategy among winning teams was to train agents to move as a group toward the central wall, then follow the wall tightly to go around it, and then proceed towards the opponents on the other side. pop and me-Rambo. These teams actively pursued their opponents by forming agents into a “pack” that had a lot of ﬁrepower and was therefore able to eliminate opponents eﬀectively.