Página 1 dos resultados de 68 itens digitais encontrados em 0.087 segundos

Evolutionary fuzzy clustering of relational data

HORTA, Danilo; ANDRADE, Ivan C. de; CAMPELLO, Ricardo J. G. B.
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.53%
This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.; CNPq; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); FAPESP

An Improved Canine Genome and a Comprehensive Catalogue of Coding Genes and Non-Coding Transcripts

Hoeppner, Marc P.; Lundquist, Andrew; Pirun, Mono; Meadows, Jennifer R. S.; Zamani, Neda; Johnson, Jeremy; Sundström, Görel; Cook, April; FitzGerald, Michael G.; Swofford, Ross; Mauceli, Evan; Moghadam, Behrooz Torabi; Greka, Anna; Alföldi, Jessica; Ab
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.41%
The domestic dog, Canis familiaris, is a well-established model system for mapping trait and disease loci. While the original draft sequence was of good quality, gaps were abundant particularly in promoter regions of the genome, negatively impacting the annotation and study of candidate genes. Here, we present an improved genome build, canFam3.1, which includes 85 MB of novel sequence and now covers 99.8% of the euchromatic portion of the genome. We also present multiple RNA-Sequencing data sets from 10 different canine tissues to catalog ∼175,000 expressed loci. While about 90% of the coding genes previously annotated by EnsEMBL have measurable expression in at least one sample, the number of transcript isoforms detected by our data expands the EnsEMBL annotations by a factor of four. Syntenic comparison with the human genome revealed an additional ∼3,000 loci that are characterized as protein coding in human and were also expressed in the dog, suggesting that those were previously not annotated in the EnsEMBL canine gene set. In addition to ∼20,700 high-confidence protein coding loci, we found ∼4,600 antisense transcripts overlapping exons of protein coding genes, ∼7,200 intergenic multi-exon transcripts without coding potential...

A survey of evolutionary algorithms for decision-tree induction

Barros, Rodrigo Coelho; Basgalupp, Márcio Porto; Carvalho, André Carlos Ponce de Leon Ferreira de; Freitas, Alex A.
Fonte: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC; PISCATAWAY Publicador: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC; PISCATAWAY
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
45.59%
This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq); Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)

A Note on Extending Taylor's Power Law for Characterizing Human Microbial Communities: Inspiration from Comparative Studies on the Distribution Patterns of Insects and Galaxies, and as a Case Study for Medical Ecology

Ma, Zhanshan Sam
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 15/05/2012 Português
Relevância na Pesquisa
55.41%
Many natural patterns, such as the distributions of blood particles in a blood sample, proteins on cell surfaces, biological populations in their habitat, galaxies in the universe, the sequence of human genes, and the fitness in evolutionary computing, have been found to follow power law. Taylor's power law (Taylor 1961: Nature, 189:732-) is well recognized as one of the fundamental models in population ecology. A fundamental property of biological populations, which Taylor's power law reveals, is the near universal heterogeneity of population abundance distribution in habitat. Obviously, the heterogeneity also exists at the community level, where not only the distributions of population abundances but also the proportions of the species composition in the community are often heterogeneous. Nevertheless, existing community diversity indexes such as Shannon index and Simpson index can only measure "local" or "static" diversity in the sense that they are computed for each habitat at a specific time point, and the indexes alone do not reflect the diversity changes. In this note, I propose to extend the application scope of Taylor's power law to the studies of human microbial communities, specifically, the community heterogeneity at both population and community levels. I further suggested that population dispersion models such as Taylor (1980: Nature...

Evolutionary Computing

Eiben, Aguston E.; Schoenauer, Marc
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/11/2005 Português
Relevância na Pesquisa
65.61%
Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly introduce the main concepts behind evolutionary computing. We present the main components all evolutionary algorithms (EA), sketch the differences between different types of EAs and survey application areas ranging from optimization, modeling and simulation to entertainment.

Artificial Immune Privileged Sites as an Enhancement to Immuno-Computing Paradigm

Chingtham, Tejbanta Singh; Sahoo, G.; Ghose, M. K.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/02/2011 Português
Relevância na Pesquisa
55.45%
The immune system is a highly parallel and distributed intelligent system which has learning, memory, and associative capabilities. Artificial Immune System is an evolutionary paradigm inspired by the biological aspects of the immune system of mammals. The immune system can inspire to form new algorithms learning from its course of action. The human immune system has motivated scientists and engineers for finding powerful information processing algorithms that has solved complex engineering problems. This work is the result of an attempt to explore a different perspective of the immune system namely the Immune Privileged Site (IPS) which has the ability to make an exception to different parts of the body by not triggering immune response to some of the foreign agent in these parts of the body. While the complete system is secured by an Immune System at certain times it may be required that the system allows certain activities which may be harmful to other system which is useful to it and learns over a period of time through the immune privilege model as done in case of Immune Privilege Sites in Natural Immune System.; Comment: Accepted for publication in International Journal of Intelligent Information Technology Application (IJIITA)

Towards a Workload for Evolutionary Analytics

LeFevre, Jeff; Sankaranarayanan, Jagan; Hacigumus, Hakan; Tatemura, Junichi; Polyzotis, Neoklis
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
45.55%
Emerging data analysis involves the ingestion and exploration of new data sets, application of complex functions, and frequent query revisions based on observing prior query answers. We call this new type of analysis evolutionary analytics and identify its properties. This type of analysis is not well represented by current benchmark workloads. In this paper, we present a workload and identify several metrics to test system support for evolutionary analytics. Along with our metrics, we present methodologies for running the workload that capture this analytical scenario.; Comment: 10 pages

A Novel Analytical Method for Evolutionary Graph Theory Problems

Shakarian, Paulo; Roos, Patrick; Moores, Geoffrey
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 11/01/2013 Português
Relevância na Pesquisa
55.56%
Evolutionary graph theory studies the evolutionary dynamics of populations structured on graphs. A central problem is determining the probability that a small number of mutants overtake a population. Currently, Monte Carlo simulations are used for estimating such fixation probabilities on general directed graphs, since no good analytical methods exist. In this paper, we introduce a novel deterministic framework for computing fixation probabilities for strongly connected, directed, weighted evolutionary graphs under neutral drift. We show how this framework can also be used to calculate the expected number of mutants at a given time step (even if we relax the assumption that the graph is strongly connected), how it can extend to other related models (e.g. voter model), how our framework can provide non-trivial bounds for fixation probability in the case of an advantageous mutant, and how it can be used to find a non-trivial lower bound on the mean time to fixation. We provide various experimental results determining fixation probabilities and expected number of mutants on different graphs. Among these, we show that our method consistently outperforms Monte Carlo simulations in speed by several orders of magnitude. Finally we show how our approach can provide insight into synaptic competition in neurology.

Competitive performance analysis of two evolutionary algorithms for routing optimization in graded network

Sooda, Kavitha; Nair, T. R. Gopalakrishnan
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/08/2014 Português
Relevância na Pesquisa
55.39%
In this paper we compare the two intelligent route generation system and its performance capability in graded networks using Artificial Bee Colony (ABC) algorithm and Genetic Algorithm (GA). Both ABC and GA have found its importance in optimization technique for determining optimal path while routing operations in the network. The paper shows how ABC approach has been utilized for determining the optimal path based on bandwidth availability of the links and determines better quality paths over GA. Here the nodes participating in the routing are evaluated for their QoS metric. The nodes which satisfy the minimum threshold value of the metric are chosen and enabled to participate in routing. A quadrant is synthesized on the source as the centre and depending on which quadrant the destination node belongs to, a search for optimal path is performed. The simulation results show that ABC speeds up local minimum search convergence by around 60% as compared to GA with respect to traffic intensity, and opens the possibility for cognitive routing in future intelligent networks.; Comment: 6 pages, 7 figures, 2 tables, 3rd IEEE International Advanced Computing Conference (IACC), 2013. arXiv admin note: text overlap with arXiv:1408.1052

Computing Agents for Decision Support Systems

Krzywicki, D.; Faber, Ł.; Byrski, A.; Kisiel-Dorohinicki, M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.51%
In decision support systems, it is essential to get a candidate solution fast, even if it means resorting to an approximation. This constraint introduces a scalability requirement with regard to the kind of heuristics which can be used in such systems. As execution time is bounded, these algorithms need to give better results and scale up with additional computing resources instead of additional time. In this paper, we show how multi-agent systems can fulfil these requirements. We recall as an example the concept of Evolutionary Multi-Agent Systems, which combine evolutionary and agent computing paradigms. We describe several possible implementations and present experimental results demonstrating how additional resources improve the efficiency of such systems.

Evolutionary Game and Learning for Dynamic Spectrum Access

Chen, Xu; Huang, Jianwei
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.51%
Efficient dynamic spectrum access mechanism is crucial for improving the spectrum utilization. In this paper, we consider the dynamic spectrum access mechanism design with both complete and incomplete network information. When the network information is available, we propose an evolutionary spectrum access mechanism. We use the replicator dynamics to study the dynamics of channel selections, and show that the mechanism achieves an equilibrium that is an evolutionarily stable strategy and is also max-min fair. With incomplete network information, we propose a distributed reinforcement learning mechanism for dynamic spectrum access. Each secondary user applies the maximum likelihood estimation method to estimate its expected payoff based on the local observations, and learns to adjust its mixed strategy for channel selections adaptively over time. We study the convergence of the learning mechanism based on the theory of stochastic approximation, and show that it globally converges to an approximate Nash equilibrium. Numerical results show that the proposed evolutionary spectrum access and distributed reinforcement learning mechanisms achieve up to 82% and 70% performance improvement than a random access mechanism, respectively, and are robust to random perturbations of channel selections.; Comment: This paper has been withdrawn by the author due to title update! A new version with the updated title "Evolutionarily Stable Spectrum Access" will be available at Arxiv

Reputation-based Mechanisms for Evolutionary Master-Worker Computing

Christoforou, Evgenia; Anta, Antonio Fernandez; Georgiou, Chryssis; Mosteiro, Miguel A.; Angel; Sanchez
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
45.59%
We consider Internet-based Master-Worker task computing systems, such as SETI@home, where a master sends tasks to potentially unreliable workers, and the workers execute and report back the result. We model such computations using evolutionary dynamics and consider three type of workers: altruistic, malicious and rational. Altruistic workers always compute and return the correct result, malicious workers always return an incorrect result, and rational (selfish) workers decide to be truthful or to cheat, based on the strategy that increases their benefit. The goal of the master is to reach eventual correctness, that is, reach a state of the computation that always receives the correct results. To this respect, we propose a mechanism that uses reinforcement learning to induce a correct behavior to rational workers; to cope with malice we employ reputation schemes. We analyze our reputation-based mechanism modeling it as a Markov chain and we give provable guarantees under which truthful behavior can be ensured. Simulation results, ob- tained using parameter values that are likely to occur in practice, reveal interesting trade-offs between various metrics, parameters and reputation types, affecting cost, time of convergence to a truthful behavior and tolerance to cheaters.; Comment: 33 pages...

Brain-Like Stochastic Search: A Research Challenge and Funding Opportunity

Werbos, Paul J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/06/2010 Português
Relevância na Pesquisa
55.39%
Brain-Like Stochastic Search (BLiSS) refers to this task: given a family of utility functions U(u,A), where u is a vector of parameters or task descriptors, maximize or minimize U with respect to u, using networks (Option Nets) which input A and learn to generate good options u stochastically. This paper discusses why this is crucial to brain-like intelligence (an area funded by NSF) and to many applications, and discusses various possibilities for network design and training. The appendix discusses recent research, relations to work on stochastic optimization in operations research, and relations to engineering-based approaches to understanding neocortex.; Comment: Plenary talk at IEEE Conference on Evolutionary Computing 1999, extended in 2010 with new appendix

Modeling and Measuring Graph Similarity: The Case for Centrality Distance

Roy, Matthieu; Schmid, Stefan; Trédan, Gilles
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 20/06/2014 Português
Relevância na Pesquisa
55.44%
The study of the topological structure of complex networks has fascinated researchers for several decades, and today we have a fairly good understanding of the types and reoccurring characteristics of many different complex networks. However, surprisingly little is known today about models to compare complex graphs, and quantitatively measure their similarity. This paper proposes a natural similarity measure for complex networks: centrality distance, the difference between two graphs with respect to a given node centrality. Centrality distances allow to take into account the specific roles of the different nodes in the network, and have many interesting applications. As a case study, we consider the closeness centrality in more detail, and show that closeness centrality distance can be used to effectively distinguish between randomly generated and actual evolutionary paths of two dynamic social networks.; Comment: FOMC 2014, 10th ACM International Workshop on Foundations of Mobile Computing, Philadelphia : United States (2014)

Elegant Object-oriented Software Design via Interactive, Evolutionary Computation

Simons, Christopher L.; Parmee, Ian C.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/10/2012 Português
Relevância na Pesquisa
55.54%
Design is fundamental to software development but can be demanding to perform. Thus to assist the software designer, evolutionary computing is being increasingly applied using machine-based, quantitative fitness functions to evolve software designs. However, in nature, elegance and symmetry play a crucial role in the reproductive fitness of various organisms. In addition, subjective evaluation has also been exploited in Interactive Evolutionary Computation (IEC). Therefore to investigate the role of elegance and symmetry in software design, four novel elegance measures are proposed based on the evenness of distribution of design elements. In controlled experiments in a dynamic interactive evolutionary computation environment, designers are presented with visualizations of object-oriented software designs, which they rank according to a subjective assessment of elegance. For three out of the four elegance measures proposed, it is found that a significant correlation exists between elegance values and reward elicited. These three elegance measures assess the evenness of distribution of (a) attributes and methods among classes, (b) external couples between classes, and (c) the ratio of attributes to methods. It is concluded that symmetrical elegance is in some way significant in software design...

Genetic Algorithm to Make Persistent Security and Quality of Image in Steganography from RS Analysis

Nair, T. R. Gopalakrishnan; V, Suma; S, Manas
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/04/2012 Português
Relevância na Pesquisa
65.45%
Retention of secrecy is one of the significant features during communication activity. Steganography is one of the popular methods to achieve secret communication between sender and receiver by hiding message in any form of cover media such as an audio, video, text, images etc. Least significant bit encoding is the simplest encoding method used by many steganography programs to hide secret message in 24bit, 8bit colour images and grayscale images. Steganalysis is a method of detecting secret message hidden in a cover media using steganography. RS steganalysis is one of the most reliable steganalysis which performs statistical analysis of the pixels to successfully detect the hidden message in an image. However, existing steganography method protects the information against RS steganalysis in grey scale images. This paper presents a steganography method using genetic algorithm to protect against the RS attack in colour images. Stego image is divided into number of blocks. Subsequently, with the implementation of natural evolution on the stego image using genetic algorithm enables to achieve optimized security and image quality.; Comment: 8 Pages, 4 Figures, Swarm Evolutionary and Memetric Computing Conference (SEMCCO), Vishakhapatnam

Syntax Evolution: Problems and Recursion

Casares, Ramón
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/08/2015 Português
Relevância na Pesquisa
55.43%
We are Turing complete, and natural language parsing is decidable, so our syntactic abilities are in excess to those needed to speak a natural language. This is an anomaly, because evolution would not keep an overqualified feature for long. We solve this anomaly by using a coincidence, both syntax and problem solving are computing, and a difference, Turing completeness is not a requirement of syntax, but of problem solving. Then computing should have been shaped by evolutionary requirements coming from both syntax and problem solving, but the last one, Turing completeness, only from problem solving. So we propose and analyze a hypothesis: syntax and problem solving co-evolved in humans towards Turing completeness. Finally, we argue that Turing completeness, also known as recursion, is our most singular feature.; Comment: 19 pages

Hybridization of Interval CP and Evolutionary Algorithms for Optimizing Difficult Problems

Vanaret, Charlie; Gotteland, Jean-Baptiste; Durand, Nicolas; Alliot, Jean-Marc
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/10/2015 Português
Relevância na Pesquisa
55.55%
The only rigorous approaches for achieving a numerical proof of optimality in global optimization are interval-based methods that interleave branching of the search-space and pruning of the subdomains that cannot contain an optimal solution. State-of-the-art solvers generally integrate local optimization algorithms to compute a good upper bound of the global minimum over each subspace. In this document, we propose a cooperative framework in which interval methods cooperate with evolutionary algorithms. The latter are stochastic algorithms in which a population of candidate solutions iteratively evolves in the search-space to reach satisfactory solutions. Within our cooperative solver Charibde, the evolutionary algorithm and the interval-based algorithm run in parallel and exchange bounds, solutions and search-space in an advanced manner via message passing. A comparison of Charibde with state-of-the-art interval-based solvers (GlobSol, IBBA, Ibex) and NLP solvers (Couenne, BARON) on a benchmark of difficult COCONUT problems shows that Charibde is highly competitive against non-rigorous solvers and converges faster than rigorous solvers by an order of magnitude.; Comment: 21st International Conference on Principles and Practice of Constraint Programming (CP 2015)...

Big Graph Search: Challenges and Techniques

Ma, Shuai; Li, Jia; Hu, Chunming; Lin, Xuelian; Huai, Jinpeng
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/11/2014 Português
Relevância na Pesquisa
55.43%
On one hand, compared with traditional relational and XML models, graphs have more expressive power and are widely used today. On the other hand, various applications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. To show this, we first introduce the application of graph search in various scenarios. We then formalize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques towards big graph search: query techniques, data techniques and distributed computing techniques.

Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model

Schmitt, Clara; Rey-Coyrehourcq, Sébastien; Reuillon, Romain; Pumain, Denise
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 24/02/2015 Português
Relevância na Pesquisa
65.51%
Multi-agent geographical models integrate very large numbers of spatial interactions. In order to validate those models large amount of computing is necessary for their simulation and calibration. Here a new data processing chain including an automated calibration procedure is experimented on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimise three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. The model is thus a useful tool for further multiple applications on empirical historical situations.