David Beasley

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A technique is described which allows unimodal function optimization methods to be extended to e ciently locate all optima of multimodal problems We describe an algorithm based on a traditional genetic algorithm GA This involves iterating the GA but uses knowledge gained during one iteration to avoid re searching on subsequent iterations regions of problem(More)
Transmission experiments are a critical component of vector competence studies. In this study, a real-time reverse transcriptase-polymerase chain reaction (RT-PCR) was used to enumerate the amount of West Nile virus (WNV) secreted in mosquito saliva following oral infection. Culex pipiens quinquefasciatus were allowed to feed on WNV-infected blood, and(More)
This paper describes a new technique for tackling highly epistatic combinatorial optimization problems. Rather than having a simple representation, simple operators, a simple fitness function, but a highly epistatic search space, this technique is intended to spread the problem’s complexity more evenly. Using our new technique, known as expansive coding,(More)
Abs t rac t . This paper describes a new technique for reducing the complexity of algorithms, such as those used in digital signal processing~ using a genetic algorithm (GA). The method, referred to as expansive coding, is a representation methodology which makes complicated combinatorim optimisation tasks easier to solve for a. GA. Using this technique,(More)
Genetic algorithms and other closely related areas such as genetic programming evolution strategies and evo lution programs are the subject of an increasing amount of research interest This two part article is intended provide an insight into this eld In the rst part of this article BBM a we described the fundamental aspects of genetic algorithms GAs We(More)
Genetic algorithm is an evolutionary approach for solving space layout and optimization problems. Due to some drawbacks in genetic algorithm, several modifications are performed on this algorithm. When the advantages of GA are combined with advantages of another algorithm then this approach is called Hybrid Genetic Algorithm. One of the most difficult(More)