Attribute Selection Algorithm with Clustering based Optimization Approach based on Mean and Similarity Distance

With hundreds or thousands of attributes in high-dimensional data, the computational workload is challenging. Attributes that have no meaningful influence on class predictions throughout the classification process increase the computing load. This article's goal is to use attribute selection to reduce the size of high-dimensional data, which will lessen the computational load. Considering selected attribute subsets that cover all attributes. As a result, there are two stages to the process: filtering out superfluous information and settling on a single attribute to stand in for a group of similar but otherwise meaningless characteristics. Numerous studies on attribute selection, including backward and forward selection, have been undertaken. This experiment and the accuracy of the categorization result recommend a k-means based PSO clustering-based attribute selection. It is likely that related attributes are present in the same cluster while irrelevant attributes are not identified in any clusters. Datasets for Credit Approval, Ionosphere, Annealing, Madelon, Isolet, and Multiple Attributes are employed alongside two other high-dimensional datasets. Both databases include the class label for each data point. Our test demonstrates that attribute selection using k-means clustering may be done to offer a subset of characteristics and that doing so produces classification outcomes that are more accurate than 80%

A New Quartet Tree Heuristic for Hierarchical Clustering

We consider the problem of constructing an an optimal-weight tree from the 3*(n choose 4) weighted quartet topologies on n objects, where optimality means that the summed weight of the embedded quartet topologiesis optimal (so it can be the case that the optimal tree embeds all quartets as non-optimal topologies). We present a heuristic for reconstructing the optimal-weight tree, and a canonical manner to derive the quartet-topology weights from a given distance matrix. The method repeatedly transforms a bifurcating tree, with all objects involved as leaves, achieving a monotonic approximation to the exact single globally optimal tree. This contrasts to other heuristic search methods from biological phylogeny, like DNAML or quartet puzzling, which, repeatedly, incrementally construct a solution from a random order of objects, and subsequently add agreement values.Comment: 22 pages, 14 figure

Group Leaders Optimization Algorithm

We present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique which is designed into a group architecture. To demonstrate the efficiency of the method, a standard suite of single and multidimensional optimization functions along with the energies and the geometric structures of Lennard-Jones clusters are given as well as the application of the algorithm on quantum circuit design problems. We show that as an improvement over previous methods, the algorithm scales as N^2.5 for the Lennard-Jones clusters of N-particles. In addition, an efficient circuit design is shown for two qubit Grover search algorithm which is a quantum algorithm providing quadratic speed-up over the classical counterpart

Cooperative co-evolutionary module identification with application to cancer disease module discovery

none10siModule identification or community detection in complex networks has become increasingly important in many scientific fields because it provides insight into the relationship and interaction between network function and topology. In recent years, module identification algorithms based on stochastic optimization algorithms such as evolutionary algorithms have been demonstrated to be superior to other algorithms on small- to medium-scale networks. However, the scalability and resolution limit (RL) problems of these module identification algorithms have not been fully addressed, which impeded their application to real-world networks. This paper proposes a novel module identification algorithm called cooperative co-evolutionary module identification to address these two problems. The proposed algorithm employs a cooperative co-evolutionary framework to handle large-scale networks. We also incorporate a recursive partitioning scheme into the algorithm to effectively address the RL problem. The performance of our algorithm is evaluated on 12 benchmark complex networks. As a medical application, we apply our algorithm to identify disease modules that differentiate low- and high-grade glioma tumors to gain insights into the molecular mechanisms that underpin the progression of glioma. Experimental results show that the proposed algorithm has a very competitive performance compared with other state-of-the-art module identification algorithms.noneHe, S and Jia, G and Zhu, Z and Tennant, DA and Huang, Q and Tang, K and Liu, J and Musolesi, M and Heath, JK and Yao, XHe, S and Jia, G and Zhu, Z and Tennant, DA and Huang, Q and Tang, K and Liu, J and Musolesi, M and Heath, JK and Yao,

Supporting 'design for reuse' with modular design

Engineering design reuse refers to the utilization of any knowledge gained from the design activity to support future design. As such, engineering design reuse approaches are concerned with the support, exploration, and enhancement of design knowledge prior, during, and after a design activity. Modular design is a product structuring principle whereby products are developed with distinct modules for rapid product development, efficient upgrades, and possible reuse (of the physical modules). The benefits of modular design center on a greater capacity for structuring component parts to better manage the relation between market requirements and the designed product. This study explores the capabilities of modular design principles to provide improved support for the engineering design reuse concept. The correlations between modular design and 'reuse' are highlighted, with the aim of identifying its potential to aid the little-supported process of design for reuse. In fulfilment of this objective the authors not only identify the requirements of design for reuse, but also propose how modular design principles can be extended to support design for reuse