Search results for: genetic similarity

Authors: Doru Anastasiu Popescu, Dan Rădulescu

Abstract:

In this paper, we determine the similarity of two HTML web applications. We are going to use a genetic algorithm in order to determine the most significant web pages of each application (we are not going to use every web page of a site). Using these significant web pages, we will find the similarity value between the two applications. The algorithm is going to be efficient because we are going to use a reduced number of web pages for comparisons but it will return an approximate value of the similarity. The binary trees are used to keep the tags from the significant pages. The algorithm was implemented in Java language.

Keywords: Tag, HTML, web page, genetic algorithm, similarity value, binary tree.

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Authors: R. C. Joshi, Shashikala Tapaswi

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The paper proposes an approach using genetic algorithm for computing the region based image similarity. The image is denoted using a set of segmented regions reflecting color and texture properties of an image. An image is associated with a family of image features corresponding to the regions. The resemblance of two images is then defined as the overall similarity between two families of image features, and quantified by a similarity measure, which integrates properties of all the regions in the images. A genetic algorithm is applied to decide the most plausible matching. The performance of the proposed method is illustrated using examples from an image database of general-purpose images, and is shown to produce good results.

Keywords: Image Features, color descriptor, segmented classes, texture descriptors, genetic algorithm.

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Authors: Ahmed A. A. Radwan, Bahgat A. Abdel Latef, Abdel Mgeid A. Ali, Osman A. Sadek

Abstract:

This study investigates the use of genetic algorithms in information retrieval. The method is shown to be applicable to three well-known documents collections, where more relevant documents are presented to users in the genetic modification. In this paper we present a new fitness function for approximate information retrieval which is very fast and very flexible, than cosine similarity fitness function.

Keywords: Cosine similarity, Fitness function, Genetic Algorithm, Information Retrieval, Query learning.

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Authors: M. F. Miah, K. M. A. Zinnah, M. J. Raihan, H. Ali, M. N. Naser

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As genetic diversity is most important for existing, breeding and production of any fish; this study was undertaken for investigating genetic diversity of freshwater mud eel, Monopterus cuchia at population level where three ecological populations such as flooded area of Sylhet (P1), open water of Moulvibazar (P2) and open water of Sunamganj (P3) districts of Bangladesh were considered. Four arbitrary RAPD primers (OPB-12, C0-4, B-03 and OPB-08) were screened and RAPD banding patterns were analyzed among the populations considering 15 individuals of each population. In total 174, 138 and 149 bands were detected in the populations of P1, P2 and P3 respectively; however, each primer revealed less number of bands in each population. 100% polymorphic loci were recorded in P2 and P3 whereas only one monomorphic locus was observed in P1, recorded 97.5% polymorphism. Different genetic parameters such as inter-individual pairwise similarity, genetic distance, Nei genetic similarity, linkage distances, cluster analysis and allelic information, etc. were considered for measuring genetic diversity. The average inter-individual pairwise similarity was recorded 2.98, 1.47 and 1.35 in P1, P2 and P3 respectively. Considering genetic distance analysis, the highest distance 1 was recorded in P2 and P3 and the lowest genetic distance 0.444 was found in P2. The average Nei genetic similarity was observed 0.19, 0.16 and 0.13 in P1, P2 and P3, respectively; however, the average linkage distance was recorded 24.92, 17.14 and 15.28 in P1, P3 and P2 respectively. Based on linkage distance, genetic clusters were generated in three populations where 6 clades and 7 clusters were found in P1, 3 clades and 5 clusters were observed in P2 and 4 clades and 7 clusters were detected in P3. In addition, allelic information was observed where the frequency of p and q alleles were observed 0.093 and 0.907 in P1, 0.076 and 0.924 in P2, 0.074 and 0.926 in P3 respectively. The average gene diversity was observed highest in P2 (0.132) followed by P3 (0.131) and P1 (0.121) respectively.

Keywords: Genetic diversity, Monopterus cuchia, population, RAPD, Bangladesh.

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Authors: Mustafa Yorgancılar, Emine Atalay, Necdet Akgün, Ali Topal

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In this study, polymerase chain reaction based Inter-simple sequence repeat (ISSR) from DNA fingerprinting techniques were used to investigate the genetic relationships among barley crossbreed genotypes in Turkey. It is important that selection based on the genetic base in breeding programs via ISSR, in terms of breeding time. 14 ISSR primers generated a total of 97 bands, of which 81 (83.35%) were polymorphic. The highest total resolution power (RP) value was obtained from the F2 (0.53) and M16 (0.51) primers. According to the ISSR result, the genetic similarity index changed between 0.64–095; Lane 3 with Line 6 genotypes were the closest, while Line 36 were the most distant ones. The ISSR markers were found to be promising for assessing genetic diversity in barley crossbreed genotypes.

Keywords: Barley, crossbreed, genetic similarity, ISSR.

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Authors: Radhwan Yousif Sedik Al-Jawadi

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Optimization is necessary for finding appropriate solutions to a range of real-life problems. In particular, genetic (or more generally, evolutionary) algorithms have proved very useful in solving many problems for which analytical solutions are not available. In this paper, we present an optimization algorithm called Dynamic Schema with Dissimilarity and Similarity of Chromosomes (DSDSC) which is a variant of the classical genetic algorithm. This approach constructs new chromosomes from a schema and pairs of existing ones by exploring their dissimilarities and similarities. To show the effectiveness of the algorithm, it is tested and compared with the classical GA, on 15 two-dimensional optimization problems taken from literature. We have found that, in most cases, our method is better than the classical genetic algorithm.

Keywords: Genetic algorithm, similarity and dissimilarity, chromosome injection, dynamic schema.

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Authors: Razib M. Othman, Safaai Deris, Rosli M. Illias, Hany T. Alashwal, Rohayanti Hassan, FarhanMohamed

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The most important property of the Gene Ontology is the terms. These control vocabularies are defined to provide consistent descriptions of gene products that are shareable and computationally accessible by humans, software agent, or other machine-readable meta-data. Each term is associated with information such as definition, synonyms, database references, amino acid sequences, and relationships to other terms. This information has made the Gene Ontology broadly applied in microarray and proteomic analysis. However, the process of searching the terms is still carried out using traditional approach which is based on keyword matching. The weaknesses of this approach are: ignoring semantic relationships between terms, and highly depending on a specialist to find similar terms. Therefore, this study combines semantic similarity measure and genetic algorithm to perform a better retrieval process for searching semantically similar terms. The semantic similarity measure is used to compute similitude strength between two terms. Then, the genetic algorithm is employed to perform batch retrievals and to handle the situation of the large search space of the Gene Ontology graph. The computational results are presented to show the effectiveness of the proposed algorithm.

Keywords: Gene Ontology, Semantic similarity measure, Genetic algorithm, Ontology search

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Authors: Vai Kuong Sin, Chon Kit Chio

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In this paper, the two-dimensional reversed stagnationpoint flow is solved by means of an anlytic approach. There are similarity solutions in case the similarity equation and the boundary condition are modified. Finite analytic method are applied to obtain the similarity velocity function.

Keywords: reversed stagnation-point flow, similarity solutions, asymptotic solution

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Authors: Bezuidt O., Lima-Mendez G., Reva O. N.

Abstract:

SeqWord Gene Island Sniffer, a new program for the identification of mobile genetic elements in sequences of bacterial chromosomes is presented. This program is based on the analysis of oligonucleotide usage variations in DNA sequences. 3,518 mobile genetic elements were identified in 637 bacterial genomes and further analyzed by sequence similarity and the functionality of encoded proteins. The results of this study are stored in an open database http://anjie.bi.up.ac.za/geidb/geidbhome. php). The developed computer program and the database provide the information valuable for further investigation of the distribution of mobile genetic elements and virulence factors among bacteria. The program is available for download at www.bi.up.ac.za/SeqWord/sniffer/index.html.

Keywords: mobile genetic elements, virulence, bacterial genomes

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Authors: Binyamin Yusoff, Imran Taib, Lazim Abdullah, Abd Fatah Wahab

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Intuitionistic fuzzy sets as proposed by Atanassov, have gained much attention from past and latter researchers for applications in various fields. Similarity measures between intuitionistic fuzzy sets were developed afterwards. However, it does not cater the conflicting behavior of each element evaluated. We therefore made some modification to the similarity measure of IFS by considering conflicting concept to the model. In this paper, we concentrate on Zhang and Fu-s similarity measures for IFSs and some examples are given to validate these similarity measures. A simple modification to Zhang and Fu-s similarity measures of IFSs was proposed to find the best result according to the use of degree of indeterminacy. Finally, we mark up with the application to real decision making problems.

Keywords: Intuitionistic fuzzy sets, similarity measures, multicriteriadecision making.

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Authors: T. Slimani, B. Ben Yaghlane, K. Mellouli

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In the field of concepts, the measure of Wu and Palmer [1] has the advantage of being simple to implement and have good performances compared to the other similarity measures [2]. Nevertheless, the Wu and Palmer measure present the following disadvantage: in some situations, the similarity of two elements of an IS-A ontology contained in the neighborhood exceeds the similarity value of two elements contained in the same hierarchy. This situation is inadequate within the information retrieval framework. To overcome this problem, we propose a new similarity measure based on the Wu and Palmer measure. Our objective is to obtain realistic results for concepts not located in the same way. The obtained results show that compared to the Wu and Palmer approach, our measure presents a profit in terms of relevance and execution time.

Keywords: Hierarchy, IS-A ontology, Semantic Web, Similarity Measure.

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Authors: G. Dimauro, S. Impedovo, M.G. Lucchese, R. Modugno, G. Pirlo

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This paper presents the analysis of similarity between local decisions, in the process of alphanumeric hand-prints classification. From the analysis of local characteristics of handprinted numerals and characters, extracted by a zoning method, the set of classification decisions is obtained and the similarity among them is investigated. For this purpose the Similarity Index is used, which is an estimator of similarity between classifiers, based on the analysis of agreements between their decisions. The experimental tests, carried out using numerals and characters from the CEDAR and ETL database, respectively, show to what extent different parts of the patterns provide similar classification decisions.

Keywords: Handwriting Recognition, Optical Character Recognition, Similarity Index, Zoning.

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Authors: Irene Luque Ruiz, Manuel Urbano Cuadrado, Miguel Ángel Gómez-Nieto

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In this paper we study different similarity based approaches for the development of QSAR model devoted to the prediction of activity of antiobesity drugs. Classical similarity approaches are compared regarding to dissimilarity models based on the consideration of the calculation of Euclidean distances between the nonisomorphic fragments extracted in the matching process. Combining the classical similarity and dissimilarity approaches into a new similarity measure, the Approximate Similarity was also studied, and better results were obtained. The application of the proposed method to the development of quantitative structure-activity relationships (QSAR) has provided reliable tools for predicting of inhibitory activity of drugs. Acceptable results were obtained for the models presented here.

Keywords: Graph similarity, Nonisomorphic dissimilarity, Approximate similarity, Drugs activity prediction.

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Authors: Adnan A. Y. Mustafa

Abstract:

In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented.

Keywords: Big images, binary images, similarity, matching.

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Authors: Madiha Khan, Sidrah Ramzan, Seemab Khan, Shahzad Hassan, Kamran Saeed

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Measuring semantic similarity between texts is calculating semantic relatedness between texts using various techniques. Our web application (Measuring Relatedness of Concepts-MRC) allows user to input two text corpuses and get semantic similarity percentage between both using WordNet. Our application goes through five stages for the computation of semantic relatedness. Those stages are: Preprocessing (extracts keywords from content), Feature Extraction (classification of words into Parts-of-Speech), Synonyms Extraction (retrieves synonyms against each keyword), Measuring Similarity (using keywords and synonyms, similarity is measured) and Visualization (graphical representation of similarity measure). Hence the user can measure similarity on basis of features as well. The end result is a percentage score and the word(s) which form the basis of similarity between both texts with use of different tools on same platform. In future work we look forward for a Web as a live corpus application that provides a simpler and user friendly tool to compare documents and extract useful information.

Keywords: GraphViz representation, semantic relatedness, similarity measurement, WordNet similarity.

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Authors: Panpan Xu, Shulin Sui

Abstract:

The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.

Keywords: Genetic algorithm, Adaptive genetic algorithm, Function optimization.

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Authors: Matthias Dehmer, Frank Emmert Streib, Alexander Mehler, Jürgen Kilian, Max Mühlhauser

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Due to the tremendous amount of information provided by the World Wide Web (WWW) developing methods for mining the structure of web-based documents is of considerable interest. In this paper we present a similarity measure for graphs representing web-based hypertext structures. Our similarity measure is mainly based on a novel representation of a graph as linear integer strings, whose components represent structural properties of the graph. The similarity of two graphs is then defined as the optimal alignment of the underlying property strings. In this paper we apply the well known technique of sequence alignments for solving a novel and challenging problem: Measuring the structural similarity of generalized trees. In other words: We first transform our graphs considered as high dimensional objects in linear structures. Then we derive similarity values from the alignments of the property strings in order to measure the structural similarity of generalized trees. Hence, we transform a graph similarity problem to a string similarity problem for developing a efficient graph similarity measure. We demonstrate that our similarity measure captures important structural information by applying it to two different test sets consisting of graphs representing web-based document structures.

Keywords: Graph similarity, hierarchical and directed graphs, hypertext, generalized trees, web structure mining.

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Authors: Matthias Dehmer, Frank Emmert Streib, Alexander Mehler, Jürgen Kilian

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Most known methods for measuring the structural similarity of document structures are based on, e.g., tag measures, path metrics and tree measures in terms of their DOM-Trees. Other methods measures the similarity in the framework of the well known vector space model. In contrast to these we present a new approach to measuring the structural similarity of web-based documents represented by so called generalized trees which are more general than DOM-Trees which represent only directed rooted trees.We will design a new similarity measure for graphs representing web-based hypertext structures. Our similarity measure is mainly based on a novel representation of a graph as strings of linear integers, whose components represent structural properties of the graph. The similarity of two graphs is then defined as the optimal alignment of the underlying property strings. In this paper we apply the well known technique of sequence alignments to solve a novel and challenging problem: Measuring the structural similarity of generalized trees. More precisely, we first transform our graphs considered as high dimensional objects in linear structures. Then we derive similarity values from the alignments of the property strings in order to measure the structural similarity of generalized trees. Hence, we transform a graph similarity problem to a string similarity problem. We demonstrate that our similarity measure captures important structural information by applying it to two different test sets consisting of graphs representing web-based documents.

Keywords: Graph similarity, hierarchical and directed graphs, hypertext, generalized trees, web structure mining.

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Authors: Guang-Ho Cha

Abstract:

This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results.

Keywords: Context-sensitive search, image search, media search, similarity ranking, similarity search.

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Authors: Guadalupe J. Torres, Ram B. Basnet, Andrew H. Sung, Srinivas Mukkamala, Bernardete M. Ribeiro

Abstract:

This paper introduces a measure of similarity between two clusterings of the same dataset produced by two different algorithms, or even the same algorithm (K-means, for instance, with different initializations usually produce different results in clustering the same dataset). We then apply the measure to calculate the similarity between pairs of clusterings, with special interest directed at comparing the similarity between various machine clusterings and human clustering of datasets. The similarity measure thus can be used to identify the best (in terms of most similar to human) clustering algorithm for a specific problem at hand. Experimental results pertaining to the text categorization problem of a Portuguese corpus (wherein a translation-into-English approach is used) are presented, as well as results on the well-known benchmark IRIS dataset. The significance and other potential applications of the proposed measure are discussed.

Keywords: Clustering Algorithms, Clustering Applications, Similarity Measures, Text Clustering

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Authors: Irene Luque Ruiz, Manuel Urbano Cuadrado, Miguel Ángel Gómez-Nieto

Abstract:

New graph similarity methods have been proposed in this work with the aim to refining the chemical information extracted from molecules matching. For this purpose, data fusion of the isomorphic and nonisomorphic subgraphs into a new similarity measure, the Approximate Similarity, was carried out by several approaches. The application of the proposed method to the development of quantitative structure-activity relationships (QSAR) has provided reliable tools for predicting several pharmacological parameters: binding of steroids to the globulin-corticosteroid receptor, the activity of benzodiazepine receptor compounds, and the blood brain barrier permeability. Acceptable results were obtained for the models presented here.

Keywords: Graph similarity, Nonisomorphic dissimilarity, Approximate similarity, Drug activity prediction.

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Authors: S. M. Khalessizadeh, R. Zaefarian, S.H. Nasseri, E. Ardil

Abstract:

Today, Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to text classification, summarization and information retrieval system in text mining process. This researches show a better performance due to the nature of Genetic Algorithm. In this paper a new algorithm for using Genetic Algorithm in concept weighting and topic identification, based on concept standard deviation will be explored.

Keywords: Genetic Algorithm, Text Mining, Term Weighting, Concept Extraction, Concept Distribution.

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Authors: Patsaraporn Somboonsak, Mud-Armeen Munlin

Abstract:

In April 2009, a new variant of Influenza A virus subtype H1N1 emerged in Mexico and spread all over the world. The influenza has three subtypes in human (H1N1, H1N2 and H3N2) Types B and C influenza tend to be associated with local or regional epidemics. Preliminary genetic characterization of the influenza viruses has identified them as swine influenza A (H1N1) viruses. Nucleotide sequence analysis of the Haemagglutinin (HA) and Neuraminidase (NA) are similar to each other and the majority of their genes of swine influenza viruses, two genes coding for the neuraminidase (NA) and matrix (M) proteins are similar to corresponding genes of swine influenza. Sequence similarity between the 2009 A (H1N1) virus and its nearest relatives indicates that its gene segments have been circulating undetected for an extended period. Nucleic acid sequence Maximum Likelihood (MCL) and DNA Empirical base frequencies, Phylogenetic relationship amongst the HA genes of H1N1 virus isolated in Genbank having high nucleotide sequence homology. In this paper we used 16 HA nucleotide sequences from NCBI for computing sequence relationships similarity of swine influenza A virus using the following method MCL the result is 28%, 36.64% for Optimal tree with the sum of branch length, 35.62% for Interior branch phylogeny Neighber – Join Tree, 1.85% for the overall transition/transversion, and 8.28% for Overall mean distance.

Keywords: Sequence DNA, Relationship of swine, Swineinfluenza, Sequence Similarity

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Authors: Patsaraporn Somboonsak, Mud-Armeen Munlin

Abstract:

The P-Bigram method is a string comparison methods base on an internal two characters-based similarity measure. The edit distance between two strings is the minimal number of elementary editing operations required to transform one string into the other. The elementary editing operations include deletion, insertion, substitution two characters. In this paper, we address the P-Bigram method to sole the similarity problem in DNA sequence. This method provided an efficient algorithm that locates all minimum operation in a string. We have been implemented algorithm and found that our program calculated that smaller distance than one string. We develop PBigram edit distance and show that edit distance or the similarity and implementation using dynamic programming. The performance of the proposed approach is evaluated using number edit and percentage similarity measures.

Keywords: Edit distance, String Matching, String Similarity

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Authors: F. Meskine, N. Taleb, M. Chikr El-Mezouar, K. Kpalma, A. Almhdie

Abstract:

Image registration is the process of establishing point by point correspondence between images obtained from a same scene. This process is very useful in remote sensing, medicine, cartography, computer vision, etc. Then, the task of registration is to place the data into a common reference frame by estimating the transformations between the data sets. In this work, we develop a rigid point registration method based on the application of genetic algorithms and Hausdorff distance. First, we extract the feature points from both images based on the algorithm of global and local curvature corner. After refining the feature points, we use Hausdorff distance as similarity measure between the two data sets and for optimizing the search space we use genetic algorithms to achieve high computation speed for its inertial parallel. The results show the efficiency of this method for registration of satellite images.

Keywords: Feature extraction, Genetic algorithms, Hausdorff distance, Image registration, Point registration.

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Authors: Roman V. Yampolskiy, Venu Govindaraju

Abstract:

Functioning of a biometric system in large part depends on the performance of the similarity measure function. Frequently a generalized similarity distance measure function such as Euclidian distance or Mahalanobis distance is applied to the task of matching biometric feature vectors. However, often accuracy of a biometric system can be greatly improved by designing a customized matching algorithm optimized for a particular biometric application. In this paper we propose a tailored similarity measure function for behavioral biometric systems based on the expert knowledge of the feature level data in the domain. We compare performance of a proposed matching algorithm to that of other well known similarity distance functions and demonstrate its superiority with respect to the chosen domain.

Keywords: Behavioral Biometrics, Euclidian Distance, Matching, Similarity Measure.

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Authors: Amod P. Vaze

Abstract:

Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to analog circuit design automation. These researches show a better performance due to the nature of Genetic Algorithm. In this paper a modified Genetic Algorithm is applied for analog circuit design automation. The modifications are made to the topology of the circuit. These modifications will lead to a more computationally efficient algorithm.

Keywords: Genetic algorithm, analog circuits, design.

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Authors: Sung Hoon Jung

Abstract:

In this paper, we propose a selective mutation method for improving the performances of genetic algorithms. In selective mutation, individuals are first ranked and then additionally mutated one bit in a part of their strings which is selected corresponding to their ranks. This selective mutation helps genetic algorithms to fast approach the global optimum and to quickly escape local optima. This results in increasing the performances of genetic algorithms. We measured the effects of selective mutation with four function optimization problems. It was found from extensive experiments that the selective mutation can significantly enhance the performances of genetic algorithms.

Keywords: Genetic algorithm, selective mutation, function optimization

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Authors: Grigorios N. Beligiannis, Georgios A. Tsirogiannis, Panayotis E. Pintelas

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In this contribution, a way to enhance the performance of the classic Genetic Algorithm is proposed. The idea of restarting a Genetic Algorithm is applied in order to obtain better knowledge of the solution space of the problem. A new operator of 'insertion' is introduced so as to exploit (utilize) the information that has already been collected before the restarting procedure. Finally, numerical experiments comparing the performance of the classic Genetic Algorithm and the Genetic Algorithm with restartings, for some well known test functions, are given.

Keywords: Genetic Algorithms, Restartings, Search space exploration, Search space exploitation.

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Authors: Bashar Al-Shboul, Sung-Hyon Myaeng

Abstract:

K-Means (KM) is considered one of the major algorithms widely used in clustering. However, it still has some problems, and one of them is in its initialization step where it is normally done randomly. Another problem for KM is that it converges to local minima. Genetic algorithms are one of the evolutionary algorithms inspired from nature and utilized in the field of clustering. In this paper, we propose two algorithms to solve the initialization problem, Genetic Algorithm Initializes KM (GAIK) and KM Initializes Genetic Algorithm (KIGA). To show the effectiveness and efficiency of our algorithms, a comparative study was done among GAIK, KIGA, Genetic-based Clustering Algorithm (GCA), and FCM [19].

Keywords: Clustering, Genetic Algorithms, K-means.

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