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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 complaints 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 desktop studying; theoretical advancements; evolutionary characteristic aid; evolutionary scheduling and combinatorial optimization; actual global functions and evolutionary snapshot 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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ACM Press, New York (1994) 7. : Co-evolution of sensor morphology and control on a simulated legged robot. In: International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007, pp. 516–521 (2007) 8. : Coevolution of form and function in the design of micro air vehicles. In: Evolvable Hardware, pp. 154–166. IEEE Computer Society (2002) 9. : Exploiting interaction between sensory morphology and learning. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol.

The 1-order Markov model captures only dependencies between adjacent variables, and the tree model can represent a maximum of n − 1 bivariate dependencies. The computational cost of EDAs is mainly associated to the methods needed to learn and sample the models. The most complex EDA used in this paper is Tree-EDA which has a computational cost O(n2 ). Examples of EDAs that use univariate, 1-order Markov, and tree models are respectively presented in [10], [4] and [1,13] and details on the methods used to learn and sample the models can be obtained from these references.

Xn ) to denote an assignment to the variables. I denotes a set of indices in {1, . . , n}, and XI (respectively xI ) a subset of the variables of X (respectively x) determined by the indices in I. In our representation for the quasiparticle braids problem, X = (X1 , . . , Xn ) represents a braid of length n, where Xi takes values in {0, 1, . . , 2g − 1} and g is the number of generators. Given an order for the generators σ1 , σ2 , . . , σg , Xi = j, j < g means that the matrix in position i is σj+1 .

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