Search results for "Fitness approximation"

showing 3 items of 3 documents

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

2017

Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approxim…

0209 industrial biotechnologyMathematical optimizationComputer scienceComputationEvolutionary algorithmComputational intelligence02 engineering and technologyMulti-objective optimizationTheoretical Computer Science020901 industrial engineering & automation0202 electrical engineering electronic engineering information engineeringmulticriteria optimizationsurrogateresponse surface approximationcomputational costmetamodelFitness approximationpareto optimalitypareto-tehokkuusFunction (mathematics)monitavoiteoptimointiFunction approximationkoneoppiminen020201 artificial intelligence & image processingGeometry and TopologySoftware
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Denoising Autoencoders for Fast Combinatorial Black Box Optimization

2015

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered pro…

FOS: Computer and information sciencesArtificial neural networkI.2.6business.industryFitness approximationComputer scienceNoise reductionI.2.8MathematicsofComputing_NUMERICALANALYSISComputer Science - Neural and Evolutionary ComputingMachine learningcomputer.software_genreAutoencoderOrders of magnitude (bit rate)Estimation of distribution algorithmBlack boxComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATIONNeural and Evolutionary Computing (cs.NE)Artificial intelligencebusinessI.2.6; I.2.8computerProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
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Genetic algorithms for 3d reconstruction with supershapes

2009

Supershape model is a recent primitive that represents numerous 3D shapes with several symmetry axes. The main interest of this model is its capability to reconstruct more complex shape than superquadric model with only one implicit equation. In this paper we propose a genetic algorithms to re-construct a point cloud using those primitives. We used the pseudo-Euclidean distance to introduce a threshold to handle real data imperfection and speed up the process. Simulations using our proposed fitness functions and a fitness function based on inside-outside function show that our fitness function based on the pseudo-Euclidean distance performs better.

Mathematical optimizationFitness functionSpeedupImplicit functionFitness approximation3D reconstructionPoint cloudFunction (mathematics)Iterative reconstructionAlgorithmMathematics2009 16th IEEE International Conference on Image Processing (ICIP)
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