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This paper investigates methods to evolve an automated agent that executes a niche trading stock strategy. Unlike trading strategies that seek to exploit broad market trends, we choose a very specific strategy on the assumption that it will be easier to learn, require less input data to do so, and more straightforward to evaluate the agents performance. In this case, we select a Low Price Recovery Strategy (LPRS), which involves picking stocks that have a high likelihood of quickly recovering after a steep, one day decline in share price. A series of intelligent agents are evolved through the use of a Genetic Programing approach. The inputs to our algorithms included traditional stock performance metrics, sentiment indicators available from online sources, and associated classification rules. The essential aspects of the research discussed include: identification of opportunities, feature selection and extraction, design of various genetic programs for evolving the agent, and testing approaches for the agents. We demonstrate that the evolved agent yields results outperform a randomized version of the LPRS and the benchmark Standard & Poor’s 500 (S&P500) stock market index.

Emerging architectures, such as the Joint Battlespace Infosphere, leverage Web and e-commerce technologies to streamline command, control and intelligence operations. Force templates provide a mechanism for seamlessly integrating diverse coalition forces into these new information systems.

A haplotype is a set of single nucleotide polymorphisms (SNPs) from a given chromosome, and provides valuable information about complex diseases. Current practices that the inferring of large scale SNP haplotypes from raw SNP data (genotypes) using computational approaches has gained a lot of attention, but it presents a grand challenges as it is inherently a NP-Hard problem. In this paper, we propose a heuristic approach, Genetic Algorithm (GA) model for the haplotypes inference method, based on the maximum-likelihood estimates of haplotype frequencies under the assumption of Hardy-Weinberg proportions. The goal of the genetic algorithm method is to obtain high prediction accuracy within a reasonable computing time. The performance of our model was evaluated on both simulated datasets and real datasets, and these results are promising, indicating that our model is a potential computational tool for haplotype inferences.

Computer viruses are widely recognised as a significant computer threat. The "birth rate" of new viruses is high and increasing due to global connectivity, and technology improvements can accelerate their spread. In response to this threat, some contemporary research efforts are aimed at creating computer virus immune systems (CVIS). A CVIS uses the human immune system as a model for identifying, attacking, and eradicating viruses from computers and networks. This paper analyses the requirements of such a computer virus immune system and evaluates current approaches with respect to these requirements. Based on this analysis, we propose a distributed architecture for implementing a CVIS. In particular, we discuss how emerging technologies such as evolutionary algorithms (EA) and intelligent agents (IA) can be employed to give the CVIS a self-adaption capability for new viral threats.

A remote monitoring system's performance is measured largely by its associated antennas' efficacy. Improving antenna performance is not a straightforward process, as many (sometimes conflicting) design trade-offs must be considered. An alternative approach specifies the desired antenna properties and then searches for antenna geometries producing those properties. We implement this approach of designing a wire antenna having optimal performance under user-defined adverse conditions by using a genetic algorithm, where the fitness of promising designs is analyzed using the Numerical Electromagnetics Code (NEC) Version 4.1. Two versions of an integrated GA-NEC code are implemented; both produce geometries showing improvements over current designs. Results for both versions are presented, compared and discussed.

One approach for rapidly implementing a prototype Joint Battlespace Infosphere (YJBI) publish and subscribe (P&S) system is to leverage the existing e-mail transport infrastructure. E-mail has the advantage of being standardized, pervasive, robust, and scalable. This Y-JBI is implemented though the peer-to-peer interaction of publisher and subscriber agents. These agent pass subscription requests, acknowledgements, and information objects to each other using Extensible Markup Language (XML) e-mail messages. Under this approach, Microsoft Exchange(Registered) is employed as the enterprise mail server with Microsoft Outlook(Registered) as the primary e-mail application servicing the JBI user. This system enables a direct flow of information from the publisher to subscriber-side fuselets that interface to Microsoft Office(Registered) planning tools.

The induction of decision rules from data is important to many disciplines, including artificial intelligence and pattern recognition. To improve the state of the art in this area, we introduced the genetic rule and classifier construction environment (GRaCCE). It was previously shown that GRaCCE consistently evolved decision rule sets from data, which were significantly more compact than those produced by other methods (such as decision tree algorithms). The primary disadvantage of GRaCCe, however, is its relatively poor run-time execution performance. In this paper, a concurrent version of the GRaCCE architecture is introduced, which improves the efficiency of the original algorithm. A prototype of the algorithm is tested on an in- house parallel processor configuration and the results are discussed.