Search results for "incremental learning"

showing 3 items of 13 documents

An adaptive probabilistic graphical model for representing skills in PbD settings

2010

business.industryComputer scienceProgramming by demonstrationBayesian probabilityProbabilistic logicMachine learningcomputer.software_genreUnsupervised learningArtificial intelligenceGraphical modelMachine Learning Imitation Learning Incremental Learning Dynamic Bayesian Network Growing Hierarchical Dynamic Bayesian NetworkAutomatic programmingbusinessHidden Markov modelcomputerDynamic Bayesian network
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A hybrid genetic algorithm with local search

2001

Abstract A hybrid genetic algorithm with internal local search was developed for optimisations involving continuous variables. The reproduction probabilities were enhanced using the fitness values obtained when a local method was applied to each individual in the population. These estimations are more realistic, since consider not the apparent but the hidden, latent quality of each individual. The information gathered in the local search was also used to build an auxiliary population recording the successfully enhanced individuals, which allowed to detect the convergence and self-adapt the search limits. The size of this auxiliary population was kept constant by a cluster analysis strategy.…

education.field_of_studyMathematical optimizationbusiness.industryProcess Chemistry and TechnologyPopulation-based incremental learningPopulationComputer Science ApplicationsAnalytical ChemistryConvergence (routing)Genetic algorithmMemetic algorithmLocal search (optimization)DeconvolutionConstant (mathematics)educationbusinessAlgorithmSpectroscopySoftwareMathematicsChemometrics and Intelligent Laboratory Systems
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Super-fit and population size reduction in compact Differential Evolution

2011

Although Differential Evolution is an efficient and versatile optimizer, it has a wide margin of improvement. During the latest years much effort of computer scientists studying Differential Evolution has been oriented towards the improvement of the algorithmic paradigm by adding and modifying components. In particular, two modifications lead to important improvements to the original algorithmic performance. The first is the super-fit mechanism, that is the injection at the beginning of the optimization process of a solution previously improved by another algorithm. The second is the progressive reduction of the population size during the evolution of the population. Recently, the algorithm…

ta113Mathematical optimizationeducation.field_of_studyMeta-optimizationFitness landscapeComputer sciencePopulation-based incremental learningPopulationContext (language use)Reduction (complexity)Differential evolutionAlgorithm designeducationAlgorithm2011 IEEE Workshop on Memetic Computing (MC)
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