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.

Meeting Name

International Joint Conference on Neural Networks, 1999. IJCNN'99.


Computer Science

Second Department

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

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 1999 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jul 1999