Generates the Ackley benchmark function. The Ackley function is a commonly used test problem for global optimization procedures.
● Data Source:
CranContrib
● Keywords: ~Optimization
● Alias: ackley
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Computes a scalar distance between the target (a set of desirable values for the responses) and the responses values that have been either observed or estimated for each point in the experimental space. Such a distance is used to identify additional experimental points to be investigated.
● Data Source:
CranContrib
● Keywords: ~Designed Experiments, ~Optimization
● Alias: distance
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The evolutionary model-based multiresponse approach (EMMA) is a procedure for process optimization and product improvement. It is particularly suited to processes featuring irregular experimental region due to constraints on the input variables (factors), multiple responses not accomodated by polynomial models, and expensive or time-consuming experiments. EMMA iterativelly selects new experimental points that increasingly concentrate on the most promising regions of the experimental space. The selection of the new experimental points is performed on the basis of the results achieved during previous trials. A multivariate adaptive regression splines (MARS) model and a particle swarm optimization (PSO) algorithm are used to drive the search of the optimum.
● Data Source:
CranContrib
● Keywords: ~Designed Experiments
● Alias: EMMA, emma-package
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EMMA designs the experiments using a procedure based on the Particle Swarm Optimization (PSO) algorithm. Firstly, EMMA selects a set of initial experimental points (see emmat0 ) that define the initial position of the particles; subsequently, for a given number of iterations, the particles are moved and new experimental points are selected (see emmatn ).
● Data Source:
CranContrib
● Keywords: ~Designed Experiments, ~Optimization
● Alias: emma
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The function evaluates if one additional experimental point is required. If this is the case, the function provides with details about the additional experiment to be performed.
● Data Source:
CranContrib
● Keywords: ~Designed Experiments, ~Optimization
● Alias: emmacheck
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The function initializes the EMMA procedure. It generates the experimental space and selects the initial set of experimental points, namely the initial set of experiments to be performed. Random sampling is used for that purpose.
● Data Source:
CranContrib
● Keywords: ~Designed Experiments, ~Optimization
● Alias: emmat0
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Given the set of experimental points investigated in previous steps of the EMMA procedure and their measured response values, emmatn returns a new set of experimental points to be investigated (and thus new experiments to be performed).
● Data Source:
CranContrib
● Keywords: ~Designed Experiments, ~Optimization
● Alias: emmatn
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Generates a benchmark function with multiple peaks.
● Data Source:
CranContrib
● Keywords: ~Optimization
● Alias: peaks
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For a problem with 1 response and 2 input variables (factors) plots a 3D graph and shows how the simulation evolves.
● Data Source:
CranContrib
● Keywords: ~Optimization
● Alias: plot.emma
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