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Random search (RS) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized, and RS can hence be used on functions that are not continuous or differentiable. Such optimization methods are also known as direct-search, derivative-free, or black-box methods. The name "random search" is attributed to Rastrigin〔 who made an early presentation of RS along with basic mathematical analysis. RS works by iteratively moving to better positions in the search-space, which are sampled from a hypersphere surrounding the current position. == Algorithm == Let ''f'': (unicode:ℝ)''n'' → (unicode:ℝ) be the fitness or cost function which must be minimized. Let x ∈ (unicode:ℝ)''n'' designate a position or candidate solution in the search-space. The basic RS algorithm can then be described as: * Initialize x with a random position in the search-space. * Until a termination criterion is met (e.g. number of iterations performed, or adequate fitness reached), repeat the following: * * Sample a new position y from the hypersphere of a given radius surrounding the current position x (see e.g. Marsaglia's technique for sampling a hypersphere.) * * If (''f''(y) < ''f''(x)) then move to the new position by setting x = y * Now x holds the best-found position. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Random search」の詳細全文を読む スポンサード リンク
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