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Several studies have applied genetic programming (GP) to the task of forecasting with favorable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new " dynamic "(More)
— This article, which lies within the data mining framework, proposes a method to build classifiers based on the evolution of rules. The method, named REC (Rule Evolution for Classifiers), has three main features: it applies genetic programming to perform a search in the space of potential solutions; a procedure allows biasing the search towards regions of(More)
A common problem in data warehousing is reduction of response time for ad hoc queries. To reduce query processing time, a selected number of views are materialised. Selecting the optimal number of views in a data warehouse is known to be an NP-complete problem as no feasible deterministic algorithm exists. In this paper, we discuss a weighted materialised(More)
Genetic Programming (GP) has proved its applicability for time series forecasting in a number of studies. The Dynamic Forecasting Genetic Program (DyFor GP) model builds on the GP technique by adding features that are tailored for the forecasting of time series whose underlying data-generating processes are non-static. Such time series often appear for(More)
1 ABSTRACT A model of strategy formulation is used to study how an adaptive attacker learns to overcome a moving target cyber defense. The attacker-defender interaction is modeled as a game in which a defender deploys a temporal platform migration defense. Against this defense, a population of attackers develop strategies specifying the temporal ordering of(More)
— This paper discusses global optimisation from a business perspective in the context of the supply chain operations. A two-silo supply chain was built for experimentation and two approaches were used for global optimisation: a classical evolutionary approach and a cooperative coevolutionary approach. The latter approach produced higher quality solutions(More)