An interactive visualization of Genetic Algorithm on 2-D graph

The visualization of search space makes it easy to understand the behavior of the Genetic Algorithms (GAs). We proposed a novel way for representation of multidimensional search space of the GA on 2-D graph. This visualization is carried out based on the gene values of the current generation, and user interruption is only required after several generations. The main contribution of this research is to propose an approach to visualize the GA search data and to improve the searching process of the GA with user's intention in different generations. Besides the selection of best individual or parents for the next generation, interference of user is required to propose a new individual in the search space. Hence, the active user intervention leads to a faster searching, resulting in less user fatigue. The experiments were carried out by evolving the parameters to derive the rules for a Parametric L-System. These rules are then used to model the growth process of branching structures in 3-D space. The experiments were conducted to evaluate the ability of the proposed approach to converge to optimized solution as compared to the Simple Genetic Algorithm (SGA).