# Discontinuum Mechanics : Using Finite and Discrete Elements by S. Mohammadi

Textbook introducing the mathematical and computational suggestions of touch mechanics that are used more and more in business and educational program of the mixed finite/discrete aspect strategy.

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Extra resources for Discontinuum Mechanics : Using Finite and Discrete Elements

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Lozano, M. (1996). Adaptation of genetic algorithm parameters based on fuzzy logic controllers, in F. Herrera & J. Verdegay (eds), Genetic Algorithms and Soft Computing, Physica-Verlag HD, pp. 95–125. Holland, J. (1992). Adaptation in Natural and Artiﬁcial Systems, MIT Press, Cambridge. Hoos, H. & Stützle, T. (2005). Stochastic Local Search. Foundations and Applications, Elsevier, Oxford. Hynen, M. (1996). Exploring phenotype space through neutral evolution, Journal of Molecular Evolution 43: 165–169.

Here, the circular symbols represent the results obtained using NSGA-II or LEA in six figures. For case 1, BNH is a two-objective function problem with a convex Pareto front and constrained conditions are two inequalities. 5. 4 36 Evolutionary Algorithms shows that although a number of optimal solutions are obtained using NSGA-II, in terms of diversity of solutions, these solutions are not evenly spread out over the entire front. 4. The Pareto front consists of x1* = x2* ∈ [0,3], x1* ∈ [3,5] and x2* =3.

Here, the notable differences between curves of both HSA-EA are not observed. To determine what impact the neutral survivor selection has on results of the HSA-EA, a comparison between results of the HSA-EA with neutral survivor selection (Neutral) and the HSA-EA with deterministic survivor selection (Deter) was done. However, both versions of the HSA-EA run without local search heuristics. Results of these are represented in the Fig. 10. As reference point, the results of the original HSA-EA hybridized with the swap local search heuristic (Re f ) that obtains the overall best results are added to the ﬁgure.