5 research outputs found
Search for optimal routes on roads applying metaheuristic algorithms
The design of efficient routes for vehicles visiting a significant number of destinations is a critical factor for the competitiveness of many companies. The design of such routes is known as the vehicle routing problem. Indeed, efficient vehicle routing is one of the most studied problems in the areas of logistics and combinatorial optimization. The present study presents a memetic algorithm that evolves using a mechanism inspired by virus mutations. Additionally, the algorithm uses Taboo Search as an intensification mechanism
Large-scale dimensionality reduction using perturbation theory and singular vectors
Massive volumes of high-dimensional data have become pervasive, with the number
of features significantly exceeding the number of samples in many applications.
This has resulted in a bottleneck for data mining applications and amplified the
computational burden of machine learning algorithms that perform classification or
pattern recognition. Dimensionality reduction can handle this problem in two ways,
i.e. feature selection (FS) and feature extraction. In this thesis, we focus on FS, because,
in many applications like bioinformatics, the domain experts need to validate
a set of original features to corroborate the hypothesis of the prediction models. In
processing the high-dimensional data, FS mainly involves detecting a limited number
of important features among tens/hundreds of thousands of irrelevant and redundant
features.
We start with filtering the irrelevant features using our proposed Sparse Least
Squares (SLS) method, where a score is assigned to each feature, and the low-scoring
features are removed using a soft threshold. To demonstrate the effectiveness of SLS,
we used it to augment the well-known FS methods, thereby achieving substantially
reduced running times while improving or at least maintaining the prediction accuracy
of the models.
We developed a linear FS method (DRPT) which, upon data reduction by SLS,
clusters the reduced data using the perturbation theory to detect correlations between
the remaining features. Important features are ultimately selected from each cluster,
discarding the redundant features.
To extend the clustering applicability in grouping the redundant features, we
proposed a new Singular Vectors FS (SVFS) method that is capable of both removing
the irrelevant features and effectively clustering the remaining features. As such,
the features in each cluster solely exhibit inner correlations with each other. The
independently selected important features from different clusters comprise the final
rank. Devising thresholds for filtering irrelevant and redundant features has facilitated
the adaptability of our model to the particular needs of various applications.
A comprehensive evaluation based on benchmark biological and image datasets
shows the superiority of our proposed methods compared to the state-of-the-art FS
methods in terms of classification accuracy, running time, and memory usage