[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Jin et al., 2004 - Google Patents

Reducing fitness evaluations using clustering techniques and neural network ensembles

Jin et al., 2004

View PDF
Document ID
16460272562266195426
Author
Jin Y
Sendhoff B
Publication year
Publication venue
Genetic and Evolutionary Computation Conference

External Links

Snippet

In many real-world applications of evolutionary computation, it is essential to reduce the number of fitness evaluations. To this end, computationally efficient models can be constructed for fitness evaluations to assist the evolutionary algorithms. When approximate …
Continue reading at www.lri.fr (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G06N3/0454—Architectures, e.g. interconnection topology using a combination of multiple neural nets
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary programming, e.g. genetic algorithms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis

Similar Documents

Publication Publication Date Title
Jin et al. Reducing fitness evaluations using clustering techniques and neural network ensembles
EP1557788B1 (en) Reduction of fitness evaluations using clustering technique and neural network ensembles
Fischer et al. A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction dataNeural network for modelling spatial interaction data
US5140530A (en) Genetic algorithm synthesis of neural networks
Kasabov et al. Quantum-inspired particle swarm optimisation for integrated feature and parameter optimisation of evolving spiking neural networks
Dioşan et al. Evolving the structure of the particle swarm optimization algorithms
Tang et al. The research on BP neural network model based on guaranteed convergence particle swarm optimization
Parvin et al. A new approach to improve the vote-based classifier selection
WO2005048185A1 (en) Transductive neuro fuzzy inference method for personalised modelling
Islam et al. Evolving artificial neural network ensembles
Hervás et al. Classification by means of evolutionary product-unit neural networks
Zarth et al. Optimization of neural networks weights and architecture: A multimodal methodology
Zhou et al. New travel demand models with back-propagation network
Lin et al. An efficient evolutionary algorithm for fuzzy inference systems
Vieira et al. Application of HLVQ and G-Prop neural networks to the problem of bankruptcy prediction
Valdez et al. A new evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms: The case of neural networks optimization
Aliev et al. Recurrent Fuzzy Neural Networks and Their Performance Analysis
Chandra et al. Multi-objective ensemble construction, learning and evolution
Alvarez-Canchila et al. Enhancing liquid state machine classification through reservoir separability optimization using swarm intelligence and multitask learning
Jaisiva et al. Computational Intelligence Theory: An Orientation Technique
Bouzerdoum et al. A generalized feedforward neural network architecture and its training using two stochastic search methods
Afonin SELECTIVE EVOLUTION CONTROL METHOD FOR EVOLUTION STRATEGIES WITH NEURAL NETWORK METAMODELS
Liu Design and Optimization of Energy Efficient Recurrent Spiking Neural Accelerators
Radev et al. Kohonen networks for self-organizing performance of two queues Markov chains
Bhattacharya An investigation on two surrogate-based EAs