By Hendrik Richter, Shengxiang Yang (auth.), Ivan Zelinka, Václav Snášel, Ajith Abraham (eds.)
Optimization difficulties have been and nonetheless are the focal point of arithmetic from antiquity to the current. because the starting of our civilization, the human race has needed to confront a variety of technological demanding situations, akin to discovering the optimum resolution of assorted difficulties together with regulate applied sciences, strength assets building, functions in economic climate, mechanical engineering and effort distribution among others. those examples surround either old in addition to sleek applied sciences just like the first electricity distribution community in united states and so forth. the various key rules formulated within the heart a while have been performed by means of Johannes Kepler (Problem of the wine barrels), Johan Bernoulli (brachystochrone problem), Leonhard Euler (Calculus of Variations), Lagrange (Principle multipliers), that have been formulated essentially within the old international and are of a geometrical nature. firstly of the fashionable period, works of L.V. Kantorovich and G.B. Dantzig (so-called linear programming) could be thought of among others. This e-book discusses a large spectrum of optimization equipment from classical to trendy, alike heuristics. Novel in addition to classical innovations can be mentioned during this booklet, together with its mutual intersection. including many fascinating chapters, a reader also will come upon quite a few tools used for proposed optimization techniques, resembling video game concept and evolutionary algorithms or modelling of evolutionary set of rules dynamics like advanced networks.
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Additional info for Handbook of Optimization: From Classical to Modern Approach
Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, New York (1996) 7. : Approximate solutions to the time–invariant Hamilton– Jacobi–Bellman equation. J. Optim. Theory Appl. 96, 589–626 (1998) 8. : Dynamic Programming, p. 1957. Princeton University Press, Princeton (2010) 9. : Dynamic memory model for non–stationary optimization. , Shackleton, M. ) Proc. Congress on Evolutionary Computation, IEEE CEC 2002, pp.
Springer, Heidelberg (2006) 81. : Evolution and maintenance of the environmental component of the phenotypic variance: Benefit of plastic traits under changing environments. P. A. Gallego, and R. Romero Abstract. This chapter presents the Bounded Dual Simplex Algorithm, which is one of the most frequently used linear programming algorithms for solving realworld problems. A solution structure of the bounded dual simplex method (used to solve linear programming problems) is presented. The main advantage of this algorithm is its use in finding solutions for large-scale problems, and its robustness and efficiency are identified in this chapter.
Springer, Heidelberg (2004) 59. : A study of dynamic severity in chaotic fitness landscapes. In: Corne, D. ) Proc. 2005 IEEE Congress on Evolutionary Computation, pp. 2824–2831. IEEE Press, Piscataway (2005) 60. : Evolutionary Optimization in Spatio–temporal Fitness Landscapes. , Yao, X. ) PPSN 2006. LNCS, vol. 4193, pp. 1–10. Springer, Heidelberg (2006) Dynamic Optimization Using Analytic and Evolutionary Approaches 27 61. : Coupled map lattices as spatio–temporal fitness functions: Landscape measures and evolutionary optimization.