Global Optimization Algorithms -- Theory and Application

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Random Search for Hyper-Parameter Optimization (2012, methodology): “General numeric methods such as simplex optimization (Nelder and Mead, 1965), constrained optimization by linear approximation (P well, 1994; Weise, 2009), finite difference stochastic approximation and simultaneous prediction stochastic approximation (Kleinman et al., 1999) could be useful, as…”
Profiling, what-if analysis, and cost-based optimization of MapReduce programs (2011, background): “There is an extensive body of work on ﬁnding good settings in complex response surfaces using techniques like simulated annealing and genetic algorithms [26].”