BibSonomyburst/user/brazovayeye/spatialBibSonomy RSS feed for /user/brazovayeye/spatial2012-02-16T19:14:32+01:00http://www.bibsonomy.org/bibtex/21b6b956c520539f09e99fde373b75900/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Coevolution, algorithms, evolution genetic programming, resource sharing, spatial <span class="authorEditorList"><a href="/author/Williams">Nathan Williams</a>, and <a href="/author/Mitchell">Melanie Mitchell</a> </span><em>GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, </em><em> 1, </em><em>page 523--530. </em><em>Washington DC, USA, </em><em>ACM Press, </em>(<em>January 2007</em>)Thu Jun 19 17:46:40 CEST 2008Washington DC, USA{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation25-29 June523--530Investigating the success of spatial coevolution12005Coevolution, algorithms, evolution genetic programming, resource sharing, spatial We investigate the results of coevolution of spatially distributed populations. In particular, we describe work in which a simple function approximation problem is used to compare different spatial evolutionary methods. Our work shows that, on this problem, spatial coevolution is dramatically more successful than any other spatial evolutionary scheme we tested. Our results support two hypotheses about the source of spatial coevolution's superior performance: (1) spatial coevolution allows population diversity to persist over many generations; and (2) spatial coevolution produces training examples ({"}parasites{"}) that specifically target weaknesses in models ({"}hosts{"}). The precise mechanisms by which the combination of spatial embedding and coevolution produces these results are still not well understood.http://www.bibsonomy.org/bibtex/2832dd0162458163f07313f825e330edb/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Fixation, Genetic Graph-based Local Moran Spatial algorithm, algorithms, drift, evolutionary genetic model, process, selection <span class="authorEditorList"><a href="/author/Whigham">P. A. Whigham</a>, and <a href="/author/Dick">Grant Dick</a> </span><em>Genetic Programming and Evolvable Machines</em> <em>9(2):157--170</em> (<em>June 2008</em>)<em>Special Issue on Theoretical foundations of evolutionary computation . </em>Thu Jun 19 17:46:40 CEST 2008Genetic Programming and Evolvable MachinesJuneSpecial Issue on Theoretical foundations of evolutionary computation2157--170Evolutionary dynamics for the spatial Moran process92008Fixation, Genetic Graph-based Local Moran Spatial algorithm, algorithms, drift, evolutionary genetic model, process, selection Evolutionary dynamics for the Moran process have been previously examined within the context of fixation behaviour for introduced mutants, where it was demonstrated that certain spatial structures act as amplifiers of selection. This article will revisit the assumptions for this spatial Moran process and show that proportional global fitness, introduced as part of the Moran process, is necessary for the amplification of selection to occur. Here it is shown that under the condition of local proportional fitness selection the amplification property no longer holds. In addition, regular structures are also shown to have a modified fixation probability from a panmictic population when local selection is applied. Theoretical results from population genetics, which suggest fixation probabilities are independent of geography, are discussed in relation to these local graph-based models and shown to have different assumptions and therefore not to be in conflict with the presented results. This paper examines the issue of fixation probability of an introduced advantageous allele in terms of spatial structure and various spatial parent selection models. The results describe the relationship between structured populations and individual selective advantage in a problem independent manner. This is of significant interest to the theory of fine-grained spatially-structured evolutionary algorithms since the interaction of selection and space for diversity maintenance, selection strength and convergence underlies resulting evolutionary trajectories.http://www.bibsonomy.org/bibtex/2cbd07bce78e305bedcdf25491884637d/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Habitat Machine Spatial algorithms, genetic learning, patterns, prediction programming, <span class="authorEditorList"><a href="/author/Whigham">P. A. Whigham</a> </span><em>Ecological Modelling</em> <em>131(2-3):299--317</em> (<em>2000</em>)Thu Jun 19 17:46:40 CEST 2008Ecological Modelling2-3299--317Induction of a marsupial density model using genetic programming and spatial relationships1312000Habitat Machine Spatial algorithms, genetic learning, patterns, prediction programming, Machine learning techniques have been developed that allow the induction of spatial models for the prediction of habitat types and population distribution. However, most learning approaches are based on a propositional language for the development of models and therefore cannot express a wide range of possible spatial relationships that exist in the data. This paper compares the application of a functional evolutionary machine learning technique for prediction of marsupial density to some standard machine learning techniques. The ability of the learning system to express spatial relationships allows an improved predictive model to be developed, which is both parsimonious and understandable. Additionally, the maps produced from this approach have a generalised appearance of the measured glider density, suggesting that the underlying preferred habitat properties of the greater glider have been identified.http://www.bibsonomy.org/bibtex/2160c07f3f871ab47885e74c23aee167f/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00Decision Neural Southern Spatial Support algorithms, bandicoot, brown distribution genetic machines, modelling networks, programming, trees, vector <span class="authorEditorList"><a href="/author/Shan">Y. Shan</a>, <a href="/author/Paull">D. Paull</a>, and <a href="/author/McKay">R. I. McKay</a> </span><em>Ecological Modelling</em> <em>195(1-2):129--138</em> (<em>March 2007</em>)<em>Selected Papers from the Third Conference of the International Society for Ecological Informatics ISEI, August 26--30, 20<span class="info">...<div>Selected Papers from the Third Conference of the International Society for Ecological Informatics ISEI, August 26--30, 2002, Grottaferrata, Rome, Italy</div></span> . </em>Thu Jun 19 17:35:00 CEST 2008Ecological Modelling15 MaySelected Papers from the Third Conference of the International Society for Ecological Informatics (ISEI), August 26--30, 2002, Grottaferrata, Rome, Italy1-2129--138Machine learning of poorly predictable ecological data1952006Decision Neural Southern Spatial Support algorithms, bandicoot, brown distribution genetic machines, modelling networks, programming, trees, vector a variety of machine learning techniques to a difficult modelling problem, the spatial distribution of an endangered Australian marsupial, the southern brown bandicoot (Isoodon obesulus). Four learning techniques decision trees/rules, neural networks, support vector machines and genetic programming were applied to the problem. Support vector and neural network approaches gave marginally better predictivity, but in the context of low overall accuracy, decision trees and genetic programming gave more useful results because of the human comprehensibility of their models.http://www.bibsonomy.org/bibtex/257144c3c78bac4463c2671918a550854/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00algorithms, generalised genetic hedonic home index, least model, prices programming, spatial squares, <span class="authorEditorList"><a href="/author/Kaboudan">Mak Mahmoud Kaboudan</a> </span><em>New Mathematics and Natural Computation</em> <em>4(2):143--163</em> (<em>July 2008</em>)Thu Jun 19 17:35:00 CEST 2008New Mathematics and Natural ComputationJuly2143--163{GP} versus {GLS} Spatial Index Models to Forecast Single-Family Home Prices42008algorithms, generalised genetic hedonic home index, least model, prices programming, spatial squares, This paper investigates use of genetic programming regression models to forecast home values. Neighbourhood prices in a city are represented by a quarterly index. Index values are ratios of each local neighborhood to the global city average real price of homes sold. Relative average neighbourhood home attributes, local socioeconomic characteristics, spatial measures, and real mortgage rates explain spatiotemporal variations in the index. To examine efficacy of model estimation, forecasts obtained using genetic programming are compared with those obtained using generalised least squares. Out-of-sample genetic programming predictions of home prices obtained using spatial index models deliver reasonable forecasts of home prices.http://www.bibsonomy.org/bibtex/263b3dbcc88eec0d6c1d0c7ab7fa16e3c/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00adapted algorithms, controllers, dynamic finite genetic machines, optimization optimizing programming, spatial state strategies systems, <span class="authorEditorList"><a href="/author/Frey">Clemens Frey</a>, and <a href="/author/Leugering">Gunter Leugering</a> </span><em>Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2001, </em><em>page 27--33. </em><em>San Francisco, California, USA, </em><em>Morgan Kaufmann, </em>(<em>July 2001</em>)Thu Jun 19 17:35:00 CEST 2008San Francisco, California, USAProceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)7-11 July27--33Evolving Strategies for Global Optimization - {A} Finite State Machine Approach2001adapted algorithms, controllers, dynamic finite genetic machines, optimization optimizing programming, spatial state strategies systems, http://www.bibsonomy.org/bibtex/2c537658326d287418ed08e6c1f67f062/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00SSIM, adaptive algorithms, filtering fuzzy genetic image index kriging, logic, measure, programming, punctual restoration, similarity spatial structure <span class="authorEditorList"><a href="/author/Chaudhry">Asmatullah Chaudhry</a>, <a href="/author/Khan">Asifullah Khan</a>, <a href="/author/Ali">Asad Ali</a>, and <a href="/author/Mirza">Anwar M. Mirza</a> </span><em>International Journal of Imaging Systems and Technology</em> <em>17(4):224--231</em> (<em>2007</em>)Thu Jun 19 17:35:00 CEST 2008International Journal of Imaging Systems and Technology4224--231A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming172007SSIM, adaptive algorithms, filtering fuzzy genetic image index kriging, logic, measure, programming, punctual restoration, similarity spatial structure We present an intelligent technique for image denoising problem of gray level images degraded with Gaussian white noise in spatial domain. The proposed technique consists of using fuzzy logic as a mapping function to decide whether a pixel needs to be krigged or not. Genetic programming is then used to evolve an optimal pixel intensity-estimation function for restoring degraded images. The proposed system has shown considerable improvement when compared both qualitatively and quantitatively with the adaptive Wiener filter, methods based on fuzzy kriging, and a fuzzy-based averaging technique. Experimental results conducted using an image database confirms that the proposed technique offers superior performance in terms of image quality measures. This also validates the use of hybrid techniques for image restoration.