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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.