Download Simulated Evolution and Learning: 10th International by Grant Dick, Will N. Browne, Peter Whigham, Mengjie Zhang, PDF

By Grant Dick, Will N. Browne, Peter Whigham, Mengjie Zhang, Lam Thu Bui, Hisao Ishibuchi, Yaochu Jin, Xiaodong Li, Yuhui Shi, Pramod Singh, Kay Chen Tan, Ke Tang

This quantity constitutes the lawsuits of the tenth foreign convention on Simulated Evolution and studying, SEAL 2012, held in Dunedin, New Zealand, in December 2014. The forty two complete papers and 29 brief papers awarded have been conscientiously reviewed and chosen from 109 submissions. The papers are prepared in topical sections on evolutionary optimization; evolutionary multi-objective optimization; evolutionary computer studying; theoretical advancements; evolutionary characteristic relief; evolutionary scheduling and combinatorial optimization; genuine international functions and evolutionary photo analysis.

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Extra resources for Simulated Evolution and Learning: 10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings

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1141, pp. 178–187. Springer, Heidelberg (1996) 11. : Beyond paired quantum Hall states: parafermions and incompressible states in the first excited Landau level. Physical Review B 59(12), 8084 (1999) 12. : Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductancebased neuron models. BMC Neuroscience 13(suppl 1), P100 (2012) 13. : Protein folding in simplified models with estimation of distribution algorithms. IEEE Transactions on Evolutionary Computation 12(4), 418–438 (2008) 14.

Schemata, distributions and graphical models in evolutionary optimization. Journal of Heuristics 5(2), 213–247 (1999) 10. : From recombination of genes to the estimation of distributions I. Binary parameters. -P. ) PPSN 1996. LNCS, vol. 1141, pp. 178–187. Springer, Heidelberg (1996) 11. : Beyond paired quantum Hall states: parafermions and incompressible states in the first excited Landau level. Physical Review B 59(12), 8084 (1999) 12. : Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductancebased neuron models.

We showed that the proposed concurrent growth method could overcome the problems of standard evolutionary strategies on a given large scale optimization task. According to the presented results we obtain a significant benefit in starting the optimization initially as simple as possible while the system undergoes a stepwise complexification during the co-evolution process. Proposed system growth approach, combined with a cost factors for a morphological dimensionality, is able to detect a minimally possible morphological configuration required to fulfill a given task of drag reduction and maintenance of a required lift.

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