Genetic Network Programming with Reconstructed Individuals

Genetic network programming (GNP) is a newly proposed evolutionary approach which can evolve itself and find the optimal solutions. It is a novel method based on the idea of genetic algorithm (GA) and uses the data structure of directed graphs. As GNP has been developed for dealing with problems in dynamic environments, many papers have demonstrated that GNP can be applied to many areas such as data mining, forecasting stock markets, elevator control systems, etc. Focusing on GNP´s distinguished expression ability of the graph structure, this paper proposes a method named genetic network programming with reconstructed individuals (GNP with RI). In the proposed method, the worst individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The enhancement of worst individuals mimics the maturing phenomenon in nature, where bad individuals can become smarter after receiving good education. GNP with RI has been applied to the the-world which is an excellent benchmark for evaluating the proposed architecture. The performance of GNP with RI is compared with conventional GNP demonstrating its superiority.