Genetic network programming with learning and evolution for adapting to dynamical environments
Shingo Mabu, Kotaro Hirasawa, Jinglu Hu · 2003
A new evolutionary algorithm named " genetic network programming, GNP" has been proposed. GNP represents its solutions as network structures, which can improve the expression and search ability. Since GA, GP, and GNP already proposed are based on evolution and they cannot change their solutions until one generation ends, we propose GNP with learning and evolution in order to adapt to a dynamical environment quickly. Learning algorithm improves search speed for solutions and evolutionary algorithm enables GNP to search wide solution space efficiently.