Abstract
This paper proposes Genetic Programming(GP) with control nodes using the conditional probability and the island model for efficient learning of agent behavior. In the methods, each individual has a chromosome representing agent behavior as several trees. In GP using the conditional probability, individuals with high fitness values are used to produce conditional probability tables to generate individuals in the next generation. In GP using the island model, the population is divided into two islands of individuals: one island keeps diversity of individuals and the other puts emphasis on the accuracy of the solution. The methods are applied to a garbage collection problem and Santa Fe Trail problem. The proposed methods are compared with traditional GP, GP with control nodes, and Genetic Network Programming(GNP) with control nodes. Experimental results show that the proposed methods are efficient.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
