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The distinction between model structure verification and model updating is logically clear. In the former, one adjusts the physics to better match experimental data; in the latter the physical parameters of the model are adjusted. The current state of the practice is to iterate back and forth between both problems until reasonable agreement with experimental data is obtained. In this work, the selection of model physics from a predefined set and the simultaneous updating of the corresponding physical parameters are investigated in an unified computational framework. A newly developed parallel (computational efficiency) multispecies (physics models) genetic algorithm is proposed and evaluated. The genetic algorithm "evolves" both model physics and parameters to improve the correlation of analytical models and corresponding test data. Two new genetic operators are developed. The first is a crossover function, which enables the sharing of the genes between two species. The second is a transmutation operator, which allows a species member to transform into a different species. The proposed algorithms are demonstrated and evaluated using three different "numerical" experiments. [ABSTRACT FROM AUTHOR]

An experiment was carried out to examine the method of adjustment when determining user preferences for the colour appearance of ambient lighting. A booth was lit using luminaires containing an array of white and coloured light emitting diodes (LEDs), allowing continuous variation of correlated colour temperature (CCT) between 2736 and 4014 K. It was predicted that the preferred CCT would be influenced by the available stimulus range, the anchor (initial setting before adjustment), adaptation time before onset of adjustment action and type of control dial. Preference was estimated for three different CCT stimulus ranges within the available range. All three ranges led to significantly different results for preferred CCT: 3288, 3490 and 3671 K. The experimental results confirmed that stimulus range, anchor and adaptation time have significant effect on the preferred CCT determined using the adjustment task, and thus, the importance of considering and reporting this information in studies that employ the method of adjustment. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2013 [ABSTRACT FROM AUTHOR]