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Inductive Logic Programming (ILP) integrates the techniques from traditional machine learning and logic programming to construct logic programs from training examples. Most existing systems employ greedy search strategies which may trap the systems in a local maxima. This paper describes a system, called the Genetic Logic Programming System (GLPS), that(More)
Genetic Programming (GP) and Inductive Logic Programming (ILP) have received increasing interest recently. Since their formalisms are so different, these two approaches cannot be integrated easily though they share many common goals and functionalities. A unification will greatly enhance their problem solving power. Moreover, they are restricted in the(More)
The knowledge acquisition bottleneck greatly obstructs the development of knowledge-based systems. One popular approach to knowledge acquisition uses inductive concept learning to derive knowledge from examples stored in databases. However, existing learning systems cannot improve themselves automatically. This paper describes an adaptive knowledge(More)
Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuits synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to(More)
In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Units (GPUs) which are available and installed on ubiquitous personal computers. HGA extends the classical genetic algorithm by incorporating the Cauchy mutation operator from evolutionary programming. In our parallel HGA, all steps except the random number(More)
This paper proposes a novel method for learning Bayesian networks from incomplete databases in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation Maximization (EM) algorithm. A data completing procedure is presented for learning and evaluating the candidate networks. Moreover, a strategy is introduced(More)
This paper describes a novel data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately , both(More)