This paper presents a genetic programming-based symbolic regression approach to the construction of relational features in link analysis applications. Specifically, we consider the problems of predicting, classifying and annotating friends relations in ...

This work outlines an evolutionary algorithm for image vector quantization. An integer-coded genetic algorithm (GA) that employs the maximum likelihood (ML) measure as the fitness function is introduced. The proposed algorithm allows for different ...

Efficient and effective deployment of IEEE 802.16 networks to service an area of users with certain traffic demands is an important network planning problem. We resort to an evolutionary approach in order to yield good approximation solutions. In our ...

It is popular that there exist multiple objectives in practical control system. To solve this problem, a dynamic multi-objective control algorithm based on NSGA-II is presented. Based on the multi-objective evolutionary algorithm and the tight relation ...

Underwater acoustic sensor deployment for military surveillance is a significant challenge due to the inherent difficulties posed by the underwater channel in terms of sensing and communications between sensors, as well as the exorbitant cost of the ...

In this work, alternative voting methods are compared to determine NASCAR rankings for the Sprint Cup Series. All of these methods make use only of the final placement of each driver in each race. We then construct a set of metrics to determine the ...

In the absence of a priori knowledge about global optima, initial populations in genetic algorithms (GAs) should at least be diversified, especially while dealing with large spaces. On the other hand, the use of parallel models for GAs helps to solve ...

In this paper, we propose a Genetic Network Programming (GNP) based ranking method to improve the accuracy of Classification Based on Association Rule(CBA). We start from an empirical phenomenon, that is, the accuracy could be improved by changing the ...

Subspace clustering algorithms in their most general form attempt to describe data with clusters that are not constrained to index a common set of attributes. Previous evolutionary approaches to this problem have assumed a weaker model in which clusters ...

One of the main problems that arises when using gene expression programming (GEP) conditions in learning classifier systems is the increasing number of symbols present as the problem size grows. When doing model-building LCS, this issue limits the ...

An appropriate pre-processing algorithm in classification is important and crucial with respect to classifier type. In this paper, two pre-processing methods are suggested to be applied before classification in order to increase classification accuracy. ...

Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a confidence measure without bias is nontrivial. An alternative approach is to ...

Neural networks are a common choice for solving classification problems, but require experimental adjustments of the topology, weights and thresholds to be effective. Success has been seen in the development of neural networks with evolutionary ...

Benchmarking of a team based model of Genetic Programming demonstrates that the naturally embedded style of feature selection is usefully extended by the teaming metaphor to provide solutions in terms of exceptionally low attribute counts. To take this ...

Bloat is a common problem with Evolutionary Algorithms (EAs) that use variable length representation. By creating unnecessarily large individuals it results in longer EA runtimes and solutions that are difficult to interpret. The causes of bloat are ...

Boundary detection constitutes a crucial step in many computer vision tasks. We present a learning approach for automatically constructing high-performance local boundary detectors for natural images via genetic programming (GP). Our GP system is unique ...

A practical method for the offline extraction and analysis of salient patterns from tree-based genetic programming (GP) individuals is proposed. The method is contrasted with Tackett's algorithm [7] and it is shown that relying solely on frequency and ...

A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions, based on the primitive features, for an image pattern recognition system on the diagnosis of the ...

Parameter control in evolutionary algorithms (EAs) has been shown to be beneficial; however, the control of offspring size has so far received very little attention. This paper introduces Futility-Based Offspring Sizing (FuBOS), a method for controlling ...