No abstract

Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool.

No abstract

This paper presents a simple

This paper presents an empirical comparison of some evolutionary algorithms to solve numerical optimization problems. The aim of the paper is to test a micro-evolutionary algorithm called Elitist evolution, originally designed to work with small populations, on a set of diverse test problems (unimodal, multimodal, separable, non-separable, shifted, and rotated) with different dimensionalities. The comparison covers micro-evolutionary algorithms based on differential evolution and particle swarm optimization. The number of successful runs, the quality of results and the computational cost, measured by the number of evaluations required to reach the vicinity of the global optimum, are used as performance criteria. Furthermore, a comparison against a state-of-the-art algorithm is presented. The obtained results suggest that the Elitist evolution is very competitive as compared with other algorithms, especially in high-dimensional search spaces.