Visualization and the understanding of multidimensional data using Genetic Algorithms: Case study of load patterns of electricity customers

Visualization is the process of transforming data, information, and knowledge into visual form, making use of humans' natural visual capabilities. Different methodologies are available for analyzing large multidimensional data sets and providing insights with respect to scientific, economic, and engineering applications. This problem has traditionally been formulated as a non-linear mathematical programming. In this paper, we formulate the data visualization problem as a quadratic assignment problem. However, this formulation is computationally difficult to solve optimally using an exact approach. Consequently, we investigate the use of the genetic algorithm for the data visualization problem. To examine capabilities of proposed method, we use a demand database by electricity customers, and compare the results with results by Self Organizing Maps (SOMs). This can be concluded that this approach generates higher quality output.