A Search for Hidden Relationships: Data Mining with Genetic Algorithms

This paper presents an algorithm that permits the search for dependencies among sets of data (univariate or multivariate time-series, or cross-sectional observations). The procedure is modeled after genetic theories and Darwinian concepts, such as natural selection and survival of the fittest. It permits the discovery of equations of the data-generating process in symbolic form. The genetic algorithm that is described here uses parts of equations as building blocks to breed ever better formulas. Apart from furnishing a deeper understanding of the dynamics of a process, the method also permits global predictions and forecasts. The algorithm is successfully tested with artificial and with economic time-series and also with cross-sectional data on the performance and salaries of NBA players during the 94–95 season.     

IMPORTANCE OF THE THERMAL CONDUCTIVITY OF LIQUID DIELECTRICS

ABSTRACT

Accurate Knowledge of the thermophysical properties of novel and conventional, new and aged, dielectric liquids is essential to give a datum upon which to estimate the thermal performance of the electrical apparatus in which they are used. The thermal conductivity of a selection of natural mineral oils used in transformers and cables has been measured using the transient line source method. The importance of accurate evaluation of thermophysical properties and of their variation with temperature is shown by comparing calculated and measured wall temperatures in a tubular heat exchanger. By making measurements in a steady state parallel plate thermal conductivity apparatus and of radiation absorption properties, the “Poltz” radiation error associated with the apparatus when it is used with diathermanous liquids is demonstrated. The interesting observation emerges that in a range of viscosity (at a specific temperature) studied, the thermal conductivity increases with decreasing viscosity.