Aspects of selection, justification and implementation of CNC machine tools in manufacturing and toolroom environments

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A qualitative study of selection systems for managers in France and Britain:the attitudes of recruiters in multinationals

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Noisy fitness evaluation in genetic algorithms and the dynamics of learning

A theoretical model is presented which describes selection in a genetic algorithm (GA) under a stochastic fitness measure and correctly accounts for finite population effects. Although this model describes a number of selection schemes, we only consider Boltzmann selection in detail here as results for this form of selection are particularly transparent when fitness is corrupted by additive Gaussian noise. Finite population effects are shown to be of fundamental importance in this case, as the noise has no effect in the infinite population limit. In the limit of weak selection we show how the effects of any Gaussian noise can be removed by increasing the population size appropriately. The theory is tested on two closely related problems: the one-max problem corrupted by Gaussian noise and generalization in a perceptron with binary weights. The averaged dynamics can be accurately modelled for both problems using a formalism which describes the dynamics of the GA using methods from statistical mechanics. The second problem is a simple example of a learning problem and by considering this problem we show how the accurate characterization of noise in the fitness evaluation may be relevant in machine learning. The training error (negative fitness) is the number of misclassified training examples in a batch and can be considered as a noisy version of the generalization error if an independent batch is used for each evaluation. The noise is due to the finite batch size and in the limit of large problem size and weak selection we show how the effect of this noise can be removed by increasing the population size. This allows the optimal batch size to be determined, which minimizes computation time as well as the total number of training examples required.

Higher-order factor structures and intercorrelations of the 16PF5 and FIRO-B

This study investigated the intercorrelations and the independent and combined factor structures of the Sixteen Personality Factor Questionnaire Fifth Edition (16PF5) and the Fundamental Interpersonal Orientation-Behaviour Scale (FIRO-B). Four thousand four hundred and fourteen U.S. participants completed these measures as part of executive assessments between 1994 and 2003. Exploratory factor analyses supported the five-factor higher-order structure of the 16PF5; however, the three-component structure for the FIRO-B was not supported. A six-factor structure was found to underlie the variance in the measures in combination. Five of these were close to the 16PF5 higher-order structure, but a sixth factor labelled Social Independence also emerged. This new factor consisted of the 16PF5 primaries of Liveliness and Social Boldness, and the FIRO-B Wanted Control scale.