The goal of minimal attribute reduction is to find the minimal subset R of the condition attribute set C such that R has the same classification quality as C. This problem is well known to be NP-hard. When only one minimal attribute reduction is required, it was transformed into a nonlinearly constrained combinatorial optimization problem over a Boolean space and some heuristic search approache...

Designing an adequate fitness function requires substantial knowledge of a problem and of features that indicate progress towards a solution. Coevolution takes the human out of the loop by dynamically constructing the evaluation function based on interactions between evolving individuals. A question is to what extent such automatic evaluation can be adequate. We define the notion of an ideal ev...

We introduce a method for segmenting a shape from an image and simultaneously determining its symmetry axis. The symmetry is used to help the segmentation and in turn the segmentation determines the symmetry. The problem is formulated as one of minimizing a goodness of fitness function and Dijkstra’s algorithm is used to find the global minimum of the cost function. The results are illustrated ...

We compare two prioritization schemes for the components of flooding vulnerability: urbanized area ration, literacy rate, mortality rate, poverty, radio/tv penetration, non-structural measures and structural measure. We prioritize the components, giving each a weight. We then express the vulnerability function as a weighted sum of its components. This weighted sum serves as the fitness function...

This paper describes an approach to the improvement of a fitness function and the optimisation of training data in genetic programming (GP) for object detection particularly object localisation problems. The fitness function uses the weighted F-measure of a genetic program and considers the localisation fitness values of the detected object locations. To investigate the training data with this ...

We have to prepare the evaluation (fitness) function to evaluate the performance of the robot when we apply the machine learning techniques to the robot application. In many cases, the fitness function is composed of several aspects. Simple implementation to cope with the multiple fitness functions is a weighted summation. This paper presents an adaptive fitness function for the evolutionary co...

Automatic path oriented test data generation is not only a crucial problem but also a hot issue in the research area of software testing today. In this paper genetic algorithm (GA) has been used as a robust metaheuristic search method under basis path testing coverage criteria. Two types of fitness function have been used, one is branch distance based fitness function (BDBFF) and other is appro...

This paper addresses the problem of quality inspection of regular textured surfaces as, e.g., encountered in industrial woven fabrics. The motivation for developing a novel approach is to utilize the template matching principle for defect detection in a way that does not need any particular statistical, structural or spectral features to be calculated during the checking phase. It is shown that...

A small gate array is evolved extrinsically to carry out a low pass filtering task defined over fifteen different frequencies. The circuit is evolved by assessing its response to digitised sine waves. Two different fitness functions are contrasted. One is based on computing the sum of the absolute differences between the actual response and that desired, the other is defined by examining charac...