Developing a Distributed Self Adaptive Genetic Algorithm with Migration for Identification of Electrical Customer Patterns

Data visualization is a key component of undirected data mining that it transforms data, information, and knowledge into visual view. In this paper, we formulate data visualization problem as a quadratic assignment problem (DV-QAP). The QAP is an NP-Hard problem and has high complexity that it is more acute for data visualization problem because it has intense dependencies among variables and big search space. Therefore, the exact approaches are inefficient to solve DV-QAP and we introduce a new technique called Distributed Self Adaptive Genetic Algorithm with Migration (DSAGAM) that their parameters adjust to increases the exploration and exploitation. This paper focuses on the effect of controlling the migration process and adjusting parameters with respect to the fitness to explore such big search spaces to improve solutions quality. Then we demonstrate the efficiency of the model for a real data set compared with the SGA, SAMGA, IGA and Sammon's mapping approaches.