Search results for " Program"

showing 10 items of 3075 documents

On the role of non-effective code in linear genetic programming

2019

In linear variants of Genetic Programming (GP) like linear genetic programming (LGP), structural introns can emerge, which are nodes that are not connected to the final output and do not contribute to the output of a program. There are claims that such non-effective code is beneficial for search, as it can store relevant and important evolved information that can be reactivated in later search phases. Furthermore, introns can increase diversity, which leads to higher GP performance. This paper studies the role of non-effective code by comparing the performance of LGP variants that deal differently with non-effective code for standard symbolic regression problems. As we find no decrease in p…

Theoretical computer scienceComputer scienceIntronContrast (statistics)Genetic programming0102 computer and information sciences02 engineering and technology01 natural sciences010201 computation theory & mathematicsLinear genetic programming0202 electrical engineering electronic engineering information engineeringCode (cryptography)020201 artificial intelligence & image processingSymbolic regressionProceedings of the Genetic and Evolutionary Computation Conference
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Dictionary-symbolwise flexible parsing

2012

AbstractLinear-time optimal parsing algorithms are rare in the dictionary-based branch of the data compression theory. A recent result is the Flexible Parsing algorithm of Matias and Sahinalp (1999) that works when the dictionary is prefix closed and the encoding of dictionary pointers has a constant cost. We present the Dictionary-Symbolwise Flexible Parsing algorithm that is optimal for prefix-closed dictionaries and any symbolwise compressor under some natural hypothesis. In the case of LZ78-like algorithms with variable costs and any, linear as usual, symbolwise compressor we show how to implement our parsing algorithm in linear time. In the case of LZ77-like dictionaries and any symbol…

Theoretical computer scienceComputer science[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS][INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS]Data_CODINGANDINFORMATIONTHEORY0102 computer and information sciences02 engineering and technologycomputer.software_genre01 natural sciencesDirected acyclic graphTheoretical Computer ScienceConstant (computer programming)020204 information systemsEncoding (memory)Optimal parsing0202 electrical engineering electronic engineering information engineeringDiscrete Mathematics and CombinatoricsStringologySymbolwise text compressionTime complexityLossless compressionParsingSettore INF/01 - InformaticaDictionary-based compressionOptimal Parsing Lossless Data Compression DAGDirected acyclic graphPrefixComputational Theory and MathematicsText compression010201 computation theory & mathematicsAlgorithmcomputerBottom-up parsingData compressionJournal of Discrete Algorithms
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Soft Pyramid Symmetry Transforms

2005

Pyramid computation is a natural paradigm of computation in planning strategies and multi-resolution image analysis. This paper introduces a new paradigm that is based on the concept of soft-hierarchical operators implemented in a pyramid architecture to retrieve global versus local symmetries. The concept of symmetry is mathematically well defined in geometry whenever patterns are crisp images (two levels). Necessity for a soft approach occurs whenever images are multi-levels and the separation between object and background is subjective or not well defined. The paper describes a new pyramid operator to detect symmetries and shows some experiments supporting the approach. This work has bee…

Theoretical computer scienceComputer sciencebusiness.industryComputationObject (computer science)Image (mathematics)Operator (computer programming)Homogeneous spacePyramidComputer visionArtificial intelligenceArchitectureSymmetry (geometry)business
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The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions.

2019

Although the field of learning automata (LA) has made significant progress in the past four decades, the LA-based methods to tackle problems involving environments with a large number of actions is, in reality, relatively unresolved. The extension of the traditional LA to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and so, most components of the vector will soon have values that are smaller than the machine accuracy permits, implying that they will never be chosen . This paper presents a solution that extends the continuous pursuit paradigm to …

Theoretical computer scienceHierarchical learning automataHierarchy (mathematics)DiscretizationLearning automataComputer Networks and CommunicationsComputer scienceLarge action numbersPursuit learning automata02 engineering and technologyVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420Probability vectorLearning automataComputer Science ApplicationsAutomatonOperator (computer programming)Artificial Intelligence0202 electrical engineering electronic engineering information engineeringBenchmark (computing)Estimator-based learning automata020201 artificial intelligence & image processingVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550SoftwareCurse of dimensionalityIEEE transactions on neural networks and learning systems
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The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions

2018

Part 10: Learning - Intelligence; International audience; Although the field of Learning Automata (LA) has made significant progress in the last four decades, the LA-based methods to tackle problems involving environments with a large number of actions are, in reality, relatively unresolved. The extension of the traditional LA (fixed structure, variable structure, discretized, and pursuit) to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and consequently, most components of the vector will, after a relatively short time, have values that are smal…

Theoretical computer scienceHierarchical learning automataHierarchy (mathematics)Learning automataComputer sciencePursuit learning automataPursuit LALearning Automata02 engineering and technologyEstimator-based LAProbability vectorField (computer science)020202 computer hardware & architectureLA with large number of actionsVariable (computer science)Operator (computer programming)Learning Automata (LA)Action (philosophy)0202 electrical engineering electronic engineering information engineeringEstimator-based learning automata[INFO]Computer Science [cs]020201 artificial intelligence & image processingHierarchical LACurse of dimensionality
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Recursive modeling for completed code generation

2009

Model-Driven Development is promising to software development because it can reduce the complexity and cost of developing large software systems. The basic idea is the use of different kinds of models during the software development process, transformations between them, and automatic code generation at the end of the development. But unlike the structural parts, fully-automated code generation from the behavior parts is still hard, if it works at all, restricted to specific application areas using a domain specific language, DSL.This paper proposes an approach to model the behavior parts of a system and to embed them into the structural models. The underlying idea is recursive refinements …

Theoretical computer scienceSource codeCode reviewbusiness.industryComputer scienceProgramming languagemedia_common.quotation_subjectSoftware developmentStatic program analysiscomputer.software_genreLinear code sequence and jumpSoftware constructionKPI-driven code analysisCode generationbusinesscomputermedia_commonProceedings of the 1st Workshop on Behaviour Modelling in Model-Driven Architecture
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Challenges of Program Synthesis with Grammatical Evolution

2020

Program synthesis is an emerging research topic in the field of EC with the potential to improve real-world software development. Grammar-guided approaches like GE are suitable for program synthesis as they can express common programming languages with their required properties. This work uses common software metrics (lines of code, McCabe metric, size and depth of the abstract syntax tree) for an analysis of GE’s search behavior and the resulting problem structure. We find that GE is not able to solve program synthesis problems, where correct solutions have higher values of the McCabe metric (which means they require conditions or loops). Since small mutations of high-quality solutions str…

Theoretical computer scienceSource lines of codebusiness.industryComputer scienceSoftware developmentGenetic programming0102 computer and information sciences02 engineering and technology01 natural sciencesSoftware metric010201 computation theory & mathematicsGrammatical evolutionMetric (mathematics)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingbusinessAbstract syntax treeProgram synthesis
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Diagrammatic approach to cellular automata and the emergence of form with inner structure

2018

We present a diagrammatic method to build up sophisticated cellular automata (CAs) as models of complex physical systems. The diagrams complement the mathematical approach to CA modeling, whose details are also presented here, and allow CAs in rule space to be classified according to their hierarchy of layers. Since the method is valid for any discrete operator and only depends on the alphabet size, the resulting conclusions, of general validity, apply to CAs in any dimension or order in time, arbitrary neighborhood ranges and topology. We provide several examples of the method, illustrating how it can be applied to the mathematical modeling of the emergence of order out of disorder. Specif…

Theoretical computer scienceStructure (category theory)Physical systemFOS: Physical sciencesPattern Formation and Solitons (nlin.PS)01 natural sciences010305 fluids & plasmasOperator (computer programming)0103 physical sciences010306 general physicsTopology (chemistry)Mathematical PhysicsMathematicsComplement (set theory)Numerical AnalysisHierarchy (mathematics)Applied MathematicsCellular Automata and Lattice Gases (nlin.CG)Mathematical Physics (math-ph)Nonlinear Sciences - Pattern Formation and SolitonsCellular automatonNonlinear Sciences - Adaptation and Self-Organizing SystemsDiagrammatic reasoningModeling and SimulationAlgorithmAdaptation and Self-Organizing Systems (nlin.AO)Nonlinear Sciences - Cellular Automata and Lattice Gases
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Representations for evolutionary algorithms

2015

Successful and efficient use of evolutionary algorithms (EA) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. The question whether a certain representation leads to better performing EAs than an alternative representation can only be answered when the operators applied are taken into consideration. The reverse is also true: deciding between alternative operators is only meaningful for a given representation. In EA practice one can distinguish two complementary approaches. The first approach uses indirect repr…

Theoretical computer sciencebusiness.industryComputer scienceEvolutionary algorithmRepresentation (systemics)Genetic programming0102 computer and information sciences02 engineering and technologyComputingMethodologies_ARTIFICIALINTELLIGENCEPhenotype01 natural sciencesOperator (computer programming)Grammatical evolution010201 computation theory & mathematicsGenetic algorithmGenotype0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingGenetic representationArtificial intelligencebusinessProceedings of the Genetic and Evolutionary Computation Conference Companion
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High Locality Representations for Automated Programming

2011

We study the locality of the genotype-phenotype mapping used in grammatical evolution (GE). GE is a variant of genetic programming that can evolve complete programs in an arbitrary language using a variable-length binary string. In contrast to standard GP, which applies search operators directly to phenotypes, GE uses an additional mapping and applies search operators to binary genotypes. Therefore, there is a large semantic gap between genotypes (binary strings) and phenotypes (programs or expressions). The case study shows that the mapping used in GE has low locality leading to low performance of standard mutation operators. The study at hand is an example of how basic design principles o…

Theoretical computer sciencebusiness.industryComputer scienceLocalityParse treeGenetic programmingcomputer.software_genreComputingMethodologies_ARTIFICIALINTELLIGENCEGrammatical evolutionLocal search (optimization)Edit distanceArtificial intelligenceHeuristicsbusinesscomputerNatural language processingSemantic gap
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