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Machine learning algorithms such as genetic programming (GP) can evolve biased classifiers when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. In this scenario, classifiers can have good accuracy on the(More)
The Hgt4 protein of Candida albicans (orf19.5962) is orthologous to the Snf3 and Rgt2 glucose sensors of Saccharomyces cerevisiae that govern sugar acquisition by regulating the expression of genes encoding hexose transporters. We found that HGT4 is required for glucose induction of the expression of HGT12, HXT10, and HGT7, which encode apparent hexose(More)
— This paper investigates improvements to the fitness function in Genetic Programming to better solve binary classification problems with unbalanced data. Data sets are unbalanced when there is a majority of examples for one particular class over the other class(es). We show that using overall classification accuracy as the fitness function evolves(More)
S. cerevisiae senses glucose and galactose differently. Glucose is detected through sensors that reside in the cellular plasma membrane. When activated, the sensors initiate a signal-transduction cascade that ultimately inactivates the Rgt1 transcriptional repressor by causing degradation of its corepressors Mth1 and Std1. This results in the expression of(More)
Genetic programming based hyper-heuristics (GPHH) have become popular over the last few years. Most of these proposed GPHH methods have focused on heuristic generation. This study investigates a new application of genetic programming (GP) in the field of hyper-heuristics and proposes a method called GPAM, which employs GP to evolve adaptive mechanisms (AM)(More)
Particle Swarm Optimisation (PSO) is an intelligent search method based on swarm intelligence and has been widely used in many fields. However it is also easily trapped in local optima. In this paper, we propose two hybrid PSO algorithms: one uses a Differential Evolution (DE) operator to replace the standard PSO method for updating a particle's position;(More)
Due-date assignment plays an important role in scheduling systems and strongly influences the delivery performance of job shops. Because of the stochastic and dynamic nature of job shops, the development of general due-date assignment models (DDAMs) is complicated. In this study, two genetic programming (GP) methods are proposed to evolve DDAMs for job shop(More)