The Clusnet Algorithm And Time Series Prediction
This paper describes a novel neural network architecture named ClusNet. This network is designed to study the trade-offs between the simplicity of instance-based methods and the accuracy of the more computational intensive learning methods. The features that make this network different from existing learning algorithms are outlined. A simple proof of convergence of the ClusNet algorithm is given. Experimental results showing the convergence of the algorithm on a specific problem is also presented. In this paper, ClusNet is applied to predict the temporal continuation of the Mackey-Glass chaotic time series. A comparison between the results obtained with ClusNet and other neural network algorithms is made. For example, ClusNet requires one-tenth the computing resources of the instance-based local linear method for this application while achieving comparable accuracy in this task. The sensitivity of ClusNet prediction accuracies on specific clustering algorithms is examined for an application. The simplicity and fast convergence of ClusNet makes it ideal as a rapid prototyping tool for applications where on-line learning is required.

title: Parameter significance estimation and financial prediction
abstract: This paper deals with the problem of parameter significance estimation, and its application to currency exchange rate prediction. The basic problem is that over the years, practitioners in the field of financial engineering have developed dozens of technical and fundamental indicators on the basis of which they try to predict financial time series. The practitioners are now faced with the problem of finding out which combinations of those indicators are most significant or relevant, and how their significance changes over time. The authors propose a novel neural architecture calledSupNet for estimating the significance of various parameters. The methodology is based on the principle of penalizing those features that are the largest contributors to the error term. Two algorithms based on this principle are proposed. This approach is different from related methodologies, which are based on the principle of removing parameters with the least significance. The proposed methodology is demonstrated on the next day returns of the DM-US currency exchange rate, and promising results are obtained.

title: Genetic Programming And Multi-agent Layered Learning By Reinforcements
abstract: We present an adaptation of the standard genetic program (GP) to hierarchically decomposable, multi-agent learning problems. To break down a problem that requires cooperation of multiple agents, we use the team objective function to derive a simpler, intermediate objective function for pairs of cooperating agents. We apply GP to optimize first for the intermediate, then for the team objective function, using the final population from the earlier GP as the initial seed population for the next. This layered learning approach facilitates the discovery of primitive behaviors that can be reused and adapted towards complex objectives based on a shared team goal. We use this method to evolve agents to play a subproblem of robotic soccer (keep-away soccer). Finally, we show how layered learning GP evolves better agents than standard GP, including GP with automatically defined functions, and how the problem decomposition results in a significant learning-speed increase.

title: An Ant Colony Approach For The Steiner Tree Problem
abstract: One ant is placed initially at each of the given terminal vertices that are to be connected. In each iteration, an ant is moved to a new location via an edge, determined stochastically, but biased in such a manner that the ants get drawn to the paths traced out by one another. Each ant maintains its own separate list of vertices already visited to avoid revisiting it. When any ant collides with another ant, or even with the path of another, it merges into the latter. An antm , currently at a vertexi , selects a vertex j not in its tabu list ) (m T , to move to, only if E j i ∈ ) , ( . In order to ensure that the ants merge with one another as quickly as possible, we define a potential for each vertex j in V , with respect to an ant m as follows,