Learning classifier systems (LCSs) are rule-based inductive learning systems that have been widely used in the field of supervised and reinforcement learning over the last few years. This paper employs sUpervised Classifier System (UCS), a supervised learning classifier system, that was introduced in 2003 for classification tasks in data mining. We present an adaptive framework of UCS on top of a self-organized map (SOM) neural network. The overall classification problem is decomposed adaptively and in real time by the SOM into subproblems, each of which is handled by a separate UCS. The framework is also tested with replacing UCS by a feedforward artificial neural network (ANN). Experiments on several synthetic and real data sets, including a very large real data set, show that the accuracy of classifications in the proposed distributed environment is as good or better than in the nondistributed environment, and execution is faster. In general, each UCS attached to a cell in the SOM has a much smaller population size than a single UCS working on the overall problem; since each data instance is exposed to a smaller population size than in the single population approach, the throughput of the overall system increases. The experiments show that the proposed framework can decompose a problem adaptively into subproblems, maintaining or improving accuracy and increasing speed.

Scenarios are pen-pictures of plausible futures, used for strategic planning. The aim of this investigation is to expand the horizon of scenario-based planning through computational models that are able to aid the analyst in the planning process. The investigation builds upon the advances of Information and Communication Technology (ICT) to create a novel, flexible and customizable computational capability-based planning methodology that is practical and theoretically sound. We will show how evolutionary computation, in particular evolutionary multi-objective optimization, can play a central role - both as an optimizer and as a source for innovation.

UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. However, the system may require a large number of rules to cover the input space. Artificial neural networks (NNs), on the other hand, normally provide a more compact representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate NNs into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action, we obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness. We also apply negative correlation learning (NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble.

Free flight is a new concept in air traffic management, where pilots are given more freedom in making decisions in the cockpit. This allows air traffic controllers to manage more flights. One of the concepts under investigation in the Australian airspace is moving sectors, where an air traffic controller becomes responsible of a moving volume of the space containing a group of airplanes. Planning flight trajectories in this group is a hard problem. In this paper, we show that XCS can be used as a reliable planning tool. We also propose a novel idea for incorporating hard constraints within XCS to increase its reliability

XCS is widely accepted as one of the most reliable Michigan-style learning classifier system for data mining. Many studies found that XCS is able to provide good generalization using a ternary representation for binary inputs as well as interval representation for continuous-valued inputs. Since distributed data mining is becoming more popular due to massive data sets spread across a network at many organizations, we have proposed an XCS system for distributed data mining called DXCS. DXCS has been tested on binary inputs. The results showed that DXCS does not only achieve as good performance as the centralized XCS system, but also reduces data transmission in the network. In this paper, we further examine DXCS with real-valued inputs in stationary and dynamic environments

XCS is a flexible system for data mining due to its ability to deal with environmental changes, learn online with little prior knowledge and evolve accurate and maximally general classifiers. In this paper, we propose DXCS which is an XCS-based distributed data mining system. A MDL metric is proposed to quantify and analyze network load, and study the balance between network load and classifier accuracy in the presence of noise. The DXCS system shows promising results.