2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)最新文献

{"title":"Co-evolutionary learning in the N-choice IPD game with PSO algorithm","authors":"Xiaoyang Wang, Huiyou Chang, Yang Yi, Yibin Lin","doi":"10.1109/NaBIC.2012.6402245","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402245","url":null,"abstract":"A particle swarm optimization (PSO) approach towards the development of strategy co-evolution for multiple choices IPD game is presented. It is demonstrated that, birds can play IPD with multiple choices, and the co-evolutionary behaviors are influenced by social environment.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132481483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extracting, identifiyng and visualisation of the content in software projects","authors":"Marek Uhlar, I. Polásek","doi":"10.1109/NaBIC.2012.6402242","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402242","url":null,"abstract":"The paper proposes a method for extracting, identifying and visualisation of topics in software projects. In addition to standard information retrieval techniques, we use AST and WordNet ontology to enrich document vectors extracted from parsed source code, LSI to reduce its dimensionality and the swarm intelligence in the bee behaviour inspired algorithms to cluster documents contained in it. We extract topics from the identified clusters and visualise them in 3D graph. The goal is to provide insight into software projects for development participants in the process of analysing and reusing the source code.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114389359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discrete Particle Swarm Optimization with local search strategy for Rule Classification","authors":"Min Chen, Simone A. Ludwig","doi":"10.1109/NaBIC.2012.6402256","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402256","url":null,"abstract":"Rule discovery is an important classification method that has been attracting a significant amount of researchers in recent years. Rule discovery or rule mining uses a set of IF-THEN rules to classify a class or category. Besides the classical approaches, many rule mining approaches use biologically-inspired algorithms such as evolutionary algorithms and swarm intelligence approaches. In this paper, a Particle Swarm Optimization based discrete implementation with a local search strategy (DPSO-LS) was devised. The local search strategy helps to overcome local optima in order to improve the solution quality. Our DPSO-LS uses the Pittsburgh approach whereby a rule base is used to represent a `particle'. This rule base is evolved over time as to find the best possible classification model. Experimental results reveal that DPSO-LS outperforms other classification methods in most cases based on rule size, TP rates, FP rates, and precision.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114916312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Taxonomy of nature inspired computational intelligence: A remote sensing perspective","authors":"Lavika Goel, D. Gupta, V. Panchal, A. Abraham","doi":"10.1109/NaBIC.2012.6402262","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402262","url":null,"abstract":"The concepts in geospatial sciences are generally vague, ambiguous and imprecise. Also, a combination of spectral, spatial and radiometric resolution of space-borne sensors presents a selective and incomplete look of the geospatial feature/object under its view from the space. Recently, the nature inspired computational intelligence (CI) techniques have emerged as an efficient mechanism to handle diverse uncertainty characteristics. This paper proposes that the human-mind model based computational intelligence techniques, the artificial immune system based computational intelligence techniques; the swarm intelligence based computational intelligence techniques and the emerging geo-sciences based intelligent techniques can be considered as the four pillars of nature inspired CI techniques and hence redefines and extends the taxonomy of nature inspired CI. Researchers have shown keen interest on the applications of natural computing in divergent domains. Scanty references are available on the applications of nature inspired computing in the area of remote sensing. We hence also propose the taxonomy of the most recent nature inspired CI techniques that have been adapted till date for geo-spatial feature extraction and analyze their performances. We also construct a technology timeline of these recent nature inspired CI techniques.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125468945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the designing perspective of Protein-Protein Interaction network using evolutionary algorithm","authors":"P. Rakshit, Archana Chowdhury, A. Konar, A. Nagar","doi":"10.1109/NaBIC.2012.6402251","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402251","url":null,"abstract":"Paradigm for studying in the new biological phenomena represented by System Biology, cellular components are not considered in isolation but as forming complex networks of relationships. Protein-Protein Interaction (PPI) networks are among the first objects studied from this new point of view. The paper addresses an interesting approach to protein-protein interaction problem using Artificial Bee Colony (ABC) optimization algorithm. In this work, PPI is formulated as an optimization problem. The binding energy and mismatch in phylogenetic profiles of two bound proteins are used as a scoring function for the solutions. Results are demonstrated for three different networks both numerically and pictorially. Experimental results reveal that the proposed method outperforms Differential Evolution (DE) based PPI network design method considering the intra- and inter-molecular energies of the evolved molecules and the phylogenetic profiles of the proteins in the network.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127874998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partitioning with Variable Neighborhood Search: A bioinspired approach","authors":"María Beatríz Bernábe Loranca, Rogelio González Velázquez, E. O. Benítez, David Pinto, J. R. Rodríguez, José Luis Martínez Flores","doi":"10.1109/NaBIC.2012.6402254","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402254","url":null,"abstract":"The artificial vision allows us to reduce a problem by means of techniques that have obeyed the study of the intelligence of living systems. A well-known technique is data mining and pattern recognition, which are disciplines dependent of artificial intelligence that from some data, allow the acquisition of knowledge and in particular, within data mining, a great application in the field of bioinformatics has been found. What is more, the big and diverse expansion of the amount of data produced by problems related to biological behavior has generated the necessity of constructing precise algorithms of prediction and classification. The precision of classification algorithms can be affected by diverse factors, some of them considered generics in any automatic learning algorithm and, therefore, applicable to the distinct research areas. These factors are the ones that have received attention in the field of automatic learning and pattern recognition, where different clustering algorithms are observed, in particular the automatic classification or better known as classification by partitions. In this scenery, is important to discover an analogy about the way that some living beings form groups to survive in their environment finding an optimal sequence or structure or, that group their objects or belongings, against a classification by partitions algorithm. The partitioning is an NP-hard problem, thus the incorporation of approximated methods is necessary. The heuristic that we expose here is Variable Neighborhood Search (VNS) focusing in the way that this heuristic does the search of neighbor conditions by means of neighborhoods to get a satisfactory solution, just like some living beings usually do it when they try to adapt to a neighborhood close to theirs or to the current space. In this work, we focus on describing in a bioinspired way, a technique of data mining known as partitional grouping with the inclusion of VNS with the purpose of finding approximated solutions for a clustering problem.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124286624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A preliminary study into the use of an evolutionary algorithm hyper-heuristic to solve the nurse rostering problem","authors":"Christopher Rae, N. Pillay","doi":"10.1109/NaBIC.2012.6402255","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402255","url":null,"abstract":"This paper reports on an initial attempt to solve the nurse rostering problem using an evolutionary algorithm selection perturbative hyper-heuristic. The main aim of this study is to get a feel for the potential of such a hyper-heuristic in solving the nurse rostering problem. This will be used to direct future extensions of this work. This study identifies low-level perturbative heuristics for this domain as well as a representation, initial population generation method, evaluation and selection methods, and genetic operator for the evolutionary algorithm hyper-heuristic. The approach was tested on six problems from the first international nurse rostering competition. The performance of the hyper-heuristic was found to be comparable to that of other methods applied to the same problems. The study has shown the potential of this approach and also identified future extensions of this work.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129174172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opposition based Chaotic Differential Evolution algorithm for solving global optimization problems","authors":"R. Thangaraj, M. Pant, T. Chelliah, A. Abraham","doi":"10.1109/NaBIC.2012.6402168","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402168","url":null,"abstract":"A modified differential evolution (DE) algorithm based on opposition based learning and chaotic sequence named Opposition based Chaotic Differential Evolution (OCDE) is proposed. The proposed OCDE algorithm is different from basic DE in two aspects. First is the generation of initial population, which follows Opposition Based Learning (OBL) rules; and the second is: dynamic adaption of scaling factor F using chaotic sequence. The numerical results obtained by OCDE when compared with the results obtained by DE and ODE (opposition based DE) algorithms on eighteen benchmark function demonstrate that the OCDE is able to find a better solution while maintaining a reasonable convergence rate.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122490822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Object shape recognition from tactile images using regional descriptors","authors":"Garima Singh, A. Jati, A. Khasnobish, S. Bhattacharyya, A. Konar, D. Tibarewala, A. Nagar","doi":"10.1109/NaBIC.2012.6402239","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402239","url":null,"abstract":"This paper presents a novel approach of shape recognition from the tactile images by touching the surface of various real life objects. Here four geometric shaped objects (viz. a planar surface, object with one edge, a cubical object i.e. object with two edges and a cylindrical object) are used for shape recognition. The high pressure regions denoting surface edges have been segmented out via multilevel thresholding. These high pressure regions hereby obtained were unique to different object classes. Some regional descriptors have been used to uniquely describe the high pressure regions. These regional descriptors have been employed as the features needed for the classification purpose. Linear Support Vector Machine (LSVM) classifier is used for object shape classification. In noise free environment the classifier gives an average accuracy of 92.6%. Some statistical tests have been performed to prove the efficacy of the classification process. The classifier performance is also tested in noisy environment with different signal-to-noise (SNR) ratios.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sampling frequency influence at fault locations using algorithms based on artificial neural networks","authors":"J. A. C. B. Silva, K. Silva, W. Neves, B. A. Souza, F. Costa","doi":"10.1109/NaBIC.2012.6402233","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402233","url":null,"abstract":"A sampling frequency evaluation used in digital fault recorders for fault locations was implemented. A chained structure of artificial neural networks (ANN) was adopted to locate the faults. The ATP (Alternative Transient Program) software was used in the building of the database for training, testing and validation of the ANN, with different sampling frequencies. The input to the ANN are phase quantities and zero sequence voltage and current waveform data. The fault conditions were simulated for a 230 kV transmission line. The database used was generated automatically from a standard format file, and run in batch mode. For the fault location, the transmission line was divided into 8 zones. Previous to location, classification of the fault type is performed by training the ANN with the full line data. For the location, eight ANN were trained for each fault type, each one with the data of each zone.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115421786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}