Xindi Cai

Time series prediction with recurrent neural networks using a hybrid PSO-EA algorithm

To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the recurrent neural network for the time series prediction. The experimental results show that our approach gives good performance in predicting the missing values from the time series.

Evolutionary swarm neural network game engine for Capture Go.

Evaluation of the current board position is critical in computer game engines. In sufficiently complex games, such a task is too difficult for a traditional brute force search to accomplish, even when combined with expert knowledge bases. This motivates the investigation of alternatives. This paper investigates the combination of neural networks, particle swarm optimization (PSO), and evolutionary algorithms (EAs) to train a board evaluator from zero knowledge. By enhancing the survivors of an EA with PSO, the hybrid algorithm successfully trains the high-dimensional neural networks to provide an evaluation of the game board through self-play. Experimental results, on the benchmark game of Capture Go, demonstrate that the hybrid algorithm can be more powerful than its individual parts, with the system playing against EA and PSO trained game engines. Also, the winning results of tournaments against a Hill-Climbing trained game engine confirm that the improvement comes from the hybrid algorithm itself. The hybrid game engine is also demonstrated against a hand-coded defensive player and a web player.

Computer Go: A Grand Challenge to AI

The oriental game of Go is among the most tantalizing unconquered challenges in artificial intelligence after IBMs DEEP BLUE beat the world Chess champion in 1997. Its high branching factor prevents the conventional tree search approach, and long-range spatiotemporal interactions make position evaluation extremely difficult. Thus, Go attracts researchers from diverse fields who are attempting to understand how computers can represent human playing and win the game against humans. Numerous publications already exist on this topic with different motivations and a variety of application contexts. This chapter surveys methods and some related works used in computer Go published from 1970 until now, and offers a basic overview for future study. We also present our attempts and simulation results in building a non-knowledge game engine, using a novel hybrid evolutionary computation algorithm, for the Capture Go game.

Training Winner-Take-All Simultaneous Recurrent Neural Networks

The winner-take-all (WTA) network is useful in database management, very large scale integration (VLSI) design, and digital processing. The synthesis procedure of WTA on single-layer fully connected architecture with sigmoid transfer function is still not fully explored. We discuss the use of simultaneous recurrent networks (SRNs) trained by Kalman filter algorithms for the task of finding the maximum among N numbers. The simulation demonstrates the effectiveness of our training approach under conditions of a shared-weight SRN architecture. A more general SRN also succeeds in solving a real classification application on car engine data