This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process. Then, it proposes two strategies to numerically optimize the crossover mask based on mathematical properties of these operators, instead of simp...

This study wants to compare the Integrated Cluster Analysis and SEM model of Warp-PLS approach with various cluster validity indices distance measures on Service Quality, Environment, Fashions, Willingness Pay, Compliant Paying Behavior Bank X Customers. The data used in this are primary. variables service quality, environment, fashion, willingness pay, compliance paying behavior at X. were obt...

Performance of conventional text based audio search engines can be improved with feature based search engines. In this paper, text independent audio ranking system for audio engines with audio signal as query is proposed. Discrete Wavelet Transform (DWT) is used for feature extraction. Ranking is obtained using three different distance metrics namely Euclidean distance, Manhattan distance and M...

In the Manhattan Sequence Consensus problem (MSC problem) we are given k integer sequences, each of length l, and we are to find an integer sequence x of length l (called a consensus sequence), such that the maximum Manhattan distance of x from each of the input sequences is minimized. For binary sequences Manhattan distance coincides with Hamming distance, hence in this case the string consens...

Swofford and Berlocher (1987) described a method for inferring trees from polymorphic character data that searches for trees that minimize the total amount of allelic frequency change as measured by the Manhattan metric. We provided three primary arguments for choosing our method over other parsimony methods proposed for polymorphic data: (1) it accommodates polymorphism without resorting to co...

This paper presents a comparative evaluation of different distance metrics for clustering data points for organ segmentation. Selecting the exact distance measure is the challenging problem in clustering. In this research work, we have compared Euclidean distance, Manhattan Distance, Minkowski distance, Chebyshev distance and Signature Quadratic form Distance measures. The main aim of this rese...

The paths of 2425 individual motorcycle trips made in London were analyzed in order to uncover the route choice decisions made by drivers. The paths were derived from global positioning system (GPS) data collected by a courier company for each of their drivers, using algorithms developed for the purpose of this paper. Motorcycle couriers were chosen due to the fact that they both know streets v...

In this paper a range of methods for measuring the phonetic distance between dialectal variants are described. It concerns variants of the frequency method, the frequency per word method and Levensh-tein distance, both simple (based on atomic characters) and complex (based on feature bundles). The measurements between feature bundles used Manhattan distance, Euclidean distance or (a measure usi...