The creation of strategies to meet abstract goals is an important behavior exhibited by natural organisms. A situation requiring the development of such strategies is the predator-evader problem. To study this problem, Khepera robots are chosen as the competing agents. Using computer simulations the evolution of the adaptive behavior is studied in a predator-evader interaction. A bilaterally symmetrical multilayer perceptron neural network architecture with evolvable weights is used to model the “brains” of the agents. Evolutionary programming is employed to evolve the predator for developing adaptive strategies to meet its goals. To study the effect of learning on evolution a self-organizing map (SOM) is added to the architecture, it is trained continuously and all the predators can access its weights. The results of these two different approaches are compared.
S. Viswanathan et al., "Evolving Neural Networks Applied to Predator-Evader Problem," Proceedings of the International Joint Conference on Neural Networks, 1999. IJCNN'99., Institute of Electrical and Electronics Engineers (IEEE), Jul 1999.
The definitive version is available at https://doi.org/10.1109/IJCNN.1999.833442
International Joint Conference on Neural Networks, 1999. IJCNN'99.
Engineering Management and Systems Engineering
Keywords and Phrases
Adaptive Systems; Digital Simulation; Mathematics Computing; Multilayer Perceptrons; Neural Net Architecture; Self-Organising Feature Maps; Evolutionary computation; Game theory; Mobile robots
Article - Conference proceedings
© 1999 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.