http://www.bibsonomy.org/burst/concept/tag/distributions,BibSonomy BuRST Feed for /concept/tag/distributions,2008-08-21T12:45:58+02:00http://www.bibsonomy.org/bibtex/2c6b0cf2cb5e153bca4b1dc0f9e64e61f/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00One-then-zeros function Series Developmental MAX VLSI Generator, Learning, Symbolic Multipopulation Neutral STGP, Program Genotype-Phenotype distributed Evolvability, Causality, Time bias, Trees, Boolean Cellular Crossover Function Arm, Animat, Inverse Mapping, BDD, Genetic Fitness, Recognition, Systems, Robotic Character Active Retina, Parallel Contour Regression, Process Turing VHDL, Discovery, Dynamic size, model, Machine machines, Reasoning, robust, Fixed algorithms, Multi-expression modular Block-oriented Artificial detection, Problem individuals, representations, structures, Hardware, landscape, mutation, robot, Complexity, Computational Layered Knowledge of Grammatical programming, Length prediction, Image Evolution problem, processing, Kinematics, Evolution, representation, Feature modelling, Linear Extraction, Pattern distributions, Evolvable genetic points, Intrinsic Polymorphism, Iterated DNA, CAD, control, design, Bloat, Controller Problem, <span style="color:#555555;">Julian <a href="http://www.bibsonomy.org/author/Miller">Miller</a> and Marco <a href="http://www.bibsonomy.org/author/Tomassini">Tomassini</a> and Pier Luca <a href="http://www.bibsonomy.org/author/Lanzi">Lanzi</a> and Conor <a href="http://www.bibsonomy.org/author/Ryan">Ryan</a> and Andrea G. B. <a href="http://www.bibsonomy.org/author/Tettamanzi">Tettamanzi</a> and William B. <a href="http://www.bibsonomy.org/author/Langdon">Langdon</a> (eds.). </span><em>volume2038ofLNCS, </em><em>Lake Como, Italy, </em><em>Springer-Verlag, </em><em>18-20 April2001. </em>Thu Jun 19 17:46:40 CEST 2008Lake Como, Italy18-20 AprilLNCSGenetic Programming, Proceedings of Euro{GP}'200120382001One-then-zeros function Series Developmental MAX VLSI Generator, Learning, Symbolic Multipopulation Neutral STGP, Program Genotype-Phenotype distributed Evolvability, Causality, Time bias, Trees, Boolean Cellular Crossover Function Arm, Animat, Inverse Mapping, BDD, Genetic Fitness, Recognition, Systems, Robotic Character Active Retina, Parallel Contour Regression, Process Turing VHDL, Discovery, Dynamic size, model, Machine machines, Reasoning, robust, Fixed algorithms, Multi-expression modular Block-oriented Artificial detection, Problem individuals, representations, structures, Hardware, landscape, mutation, robot, Complexity, Computational Layered Knowledge of Grammatical programming, Length prediction, Image Evolution problem, processing, Kinematics, Evolution, representation, Feature modelling, Linear Extraction, Pattern distributions, Evolvable genetic points, Intrinsic Polymorphism, Iterated DNA, CAD, control, design, Bloat, Controller Problem, http://www.bibsonomy.org/bibtex/27f8a3f967a63031241546d6af058a011/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00schema crossover, programming, theory, variable-length homologous recombination algorithm, distributions, genetic algorithms, crossover masks <span style="color:#555555;">Riccardo <a href="http://www.bibsonomy.org/author/Poli">Poli</a> and Nicholas Freitag <a href="http://www.bibsonomy.org/author/McPhee">McPhee</a> </span><em>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), </em><em>page104--111. </em><em>San Francisco, California, USA, </em><em>Morgan Kaufmann, </em><em>7-11 July2001. </em>Thu Jun 19 17:46:40 CEST 2008San Francisco, California, USAProceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)7-11 July104--111Exact Schema Theory for {GP} and Variable-length {GA}s with Homologous Crossover2001schema crossover, programming, theory, variable-length homologous recombination algorithm, distributions, genetic algorithms, crossover masks http://www.bibsonomy.org/bibtex/26cb86941c18b04877ae431bc859f4d74/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00bloat Depth limits, genetic Size algorithms, Population programming, Tree distributions, Shape, <span style="color:#555555;">Ellery Fussell <a href="http://www.bibsonomy.org/author/Crane">Crane</a> and Nicholas Freitag <a href="http://www.bibsonomy.org/author/McPhee">McPhee</a> </span><em>Genetic Programming Theory and Practice III, </em><em>volume9ofGenetic Programming, </em><em>chapter 15, </em><em>Springer, </em><em>Ann Arbor, </em><em>12-14 May2005. </em>Thu Jun 19 17:35:00 CEST 2008Ann ArborGenetic Programming Theory and Practice {III}1512-14 May223--240Genetic ProgrammingThe effects of size and depth limits on tree based genetic programming92005bloat Depth limits, genetic Size algorithms, Population programming, Tree distributions, Shape, Bloat is a common and well studied problem in genetic programming. Size and depth limits are often used to combat bloat, but to date there has been little detailed exploration of the effects and biases of such limits. In this paper we present empirical analysis of the effects of size and depth limits on binary tree genetic programs. We find that size limits control population average size in much the same way as depth limits do. Our data suggests, however that size limits provide finer and more reliable control than depth limits, which has less of an impact upon tree shapes.http://www.bibsonomy.org/bibtex/2cb75c5a1c05cce18dc187c31efb994b1/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00algorithms, programming, density fitness genetic gradient-based estimation, distributions, operators <span style="color:#555555;">Christian <a href="http://www.bibsonomy.org/author/Igel">Igel</a> and Martin <a href="http://www.bibsonomy.org/author/Kreutz">Kreutz</a> </span><em>Proceedings of the Congress on Evolutionary Computation, </em><em>3, </em><em>page1902--1909. </em><em>Mayflower Hotel, Washington D.C., USA, </em><em>IEEE Press, </em><em>6-9 July1999. </em>Thu Jun 19 17:35:00 CEST 2008Mayflower Hotel, Washington D.C., USAProceedings of the Congress on Evolutionary Computation6-9 July1902--1909Using Fitness Distributions to Improve the Evolution of Learning Structures31999algorithms, programming, density fitness genetic gradient-based estimation, distributions, operators the absolute benefit, a measure of improvement in the fitness space, is derived from the viewpoint of fitness distribution and fitness trajectory analysis. It is used for online operator-adaptation, where the optimisation of density estimation models serves as an example. A new information theory based measure is proposed to judge the accuracy of the evolved models. Further, the absolute benefit is applied to offline analysis of new gradient based operators used for coefficient adaptation in genetic programming. An efficient method to calculate the gradient information is presented.http://www.bibsonomy.org/bibtex/2de664b433df376e01028cd09d21855ba/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00One-then-zeros problem glitches, distributions, Linear Schema algorithms, programming, genetic landscape Length theory, representations, Bloat, Fitness <span style="color:#555555;">Nicholas Freitag <a href="http://www.bibsonomy.org/author/McPhee">McPhee</a> and Riccardo <a href="http://www.bibsonomy.org/author/Poli">Poli</a> </span><em>Genetic Programming, Proceedings of EuroGP'2001, </em><em>volume2038ofLNCS, </em><em>page108--125. </em><em>Lake Como, Italy, </em><em>Springer-Verlag, </em><em>18-20 April2001. </em>Thu Jun 19 17:35:00 CEST 2008Lake Como, ItalyGenetic Programming, Proceedings of EuroGP'200118-20 April108--125LNCSA schema theory analysis of the evolution of size in genetic programming with linear representations20382001One-then-zeros problem glitches, distributions, Linear Schema algorithms, programming, genetic landscape Length theory, representations, Bloat, Fitness In this paper we use the schema theory presented elsewhere in this volume to better understand the changes in size distribution when using GP with standard crossover and linear structures. Applications of the theory to problems both with and without fitness suggest that standard crossover induces specific biases in the distributions of sizes, with a strong tendency to over sample small structures, and indicate the existence of strong redistribution effects that may be a major force in the early stages of a GP run. We also present two important theoretical results: An exact theory of bloat, and a general theory of how average size changes on flat landscapes with glitches. The latter implies the surprising result that a single program glitch in an otherwise flat fitness landscape is sufficient to drive the average program size of an infinite population, which may have important implications for the control of code growth.http://www.bibsonomy.org/bibtex/22205881d024c913a7d4ea58ec9b3d196/idsiaidsia2008-03-11T14:52:34+01:00algorithmic, martin-lof, theory, randomness, information, universal enumerable, juergen, quasimeasures, sequence, mixture, prediction, distributions, convergence, posterior, semimeasure, <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Hutter">Hutter</a> and <a href="http://www.bibsonomy.org/author/An">An</a> </span><em>Proc. 15th International Conf. on Algorithmic Learning Theory (ALT'04), </em><em>volume3244ofLNAI, </em><em>page234--248. </em><em>Padova, </em><em>Springer, Berlin, </em>(<em>2004</em>)Tue Mar 11 14:52:34 CET 2008PadovaProc. 15th International Conf. on Algorithmic Learning Theory ({ALT'04})234--248LNAIUniversal Convergence of Semimeasures on Individual Random Sequences32442004algorithmic, martin-lof, theory, randomness, information, universal enumerable, juergen, quasimeasures, sequence, mixture, prediction, distributions, convergence, posterior, semimeasure, http://www.bibsonomy.org/bibtex/2f02ccc4a785f11b569dbd098e189c91e/idsiaidsia2008-03-11T14:52:34+01:00juergen, posterior, universal prediction, mixture, distributions, theory, solomonoffs, algorithmic, probability, information, martin-loef, computability, sequence, prior, randomness, convergence, concepts, <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Hutter">Hutter</a> </span><em>Theoretical Computer Science</em>(<em>2005</em>)Tue Mar 11 14:52:34 CET 2008Theoretical Computer ScienceOn Generalized Computable Universal Priors and their Convergence2005juergen, posterior, universal prediction, mixture, distributions, theory, solomonoffs, algorithmic, probability, information, martin-loef, computability, sequence, prior, randomness, convergence, concepts, idsiahttp://www.bibsonomy.org/bibtex/22205881d024c913a7d4ea58ec9b3d196/schaulschaul2008-02-26T11:58:58+01:00juergen, martin-lof, quasimeasures, convergence, enumerable, mixture, semimeasure, distributions, randomness, theory, information, universal posterior, prediction, algorithmic, sequence, <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Hutter">Hutter</a> and <a href="http://www.bibsonomy.org/author/An">An</a> </span><em>Proc. 15th International Conf. on Algorithmic Learning Theory (ALT'04), </em><em>volume3244ofLNAI, </em><em>page234--248. </em><em>Padova, </em><em>Springer, Berlin, </em>(<em>2004</em>)Tue Feb 26 11:58:58 CET 2008PadovaProc. 15th International Conf. on Algorithmic Learning Theory ({ALT'04})234--248LNAIUniversal Convergence of Semimeasures on Individual Random Sequences32442004juergen, martin-lof, quasimeasures, convergence, enumerable, mixture, semimeasure, distributions, randomness, theory, information, universal posterior, prediction, algorithmic, sequence, idsiahttp://www.bibsonomy.org/bibtex/2f02ccc4a785f11b569dbd098e189c91e/schaulschaul2008-02-26T11:58:58+01:00probability, posterior, prediction, convergence, distributions, algorithmic, concepts, juergen, mixture, randomness, sequence, martin-loef, solomonoffs, computability, theory, universal information, prior, <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Hutter">Hutter</a> </span><em>Theoretical Computer Science</em>(<em>2005</em>)Tue Feb 26 11:58:58 CET 2008Theoretical Computer ScienceOn Generalized Computable Universal Priors and their Convergence2005probability, posterior, prediction, convergence, distributions, algorithmic, concepts, juergen, mixture, randomness, sequence, martin-loef, solomonoffs, computability, theory, universal information, prior,