Kenneth, O.S. and Miikkulainen, R. (2002) Efficient Evolution of Neural Network Topologies. Proceedings of the 2002 Congress on Evolutionary Computation (CEC’02). In: Langdon, W.B., Cantú-Paz, E., Mathias, K.E., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M. A., Schultz, A.C., Miller, J.F., Burke, E.K. and Jonoska, N., Eds., GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, 9-13 July 2002, 569-577.

has been cited by the following article:

• TITLE: Evolutionary Algorithm Based Approach for Modeling Autonomously Trading Agents

  AUTHORS: Anil Yaman, Stephen Lucci, Izidor Gertner

  KEYWORDS: Artificial Intelligence; Autonomous Agents; Artificial Life; Evolutionary Computation; Neural Networks; FOREX

  JOURNAL NAME: Intelligent Information Management, Vol.6 No.2, March 28, 2014

  ABSTRACT: The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State Network (ESNs) model. We generate a collection of trading agents that use different trading strategies using Evolutionary Programming (EP). The agents are tested on EUR/ USD real market data. The main goal of this study is to test the overall performance of this collection of agents when they are active simultaneously. Simulation results show that using different agents concurrently outperform a single agent acting alone.