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Evolutionary artificial neural network based on Chemical Reaction Optimization

https://doi.org/10.1109/cec.2011.5949872
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Evolutionary computation: Toward a new philosophy of machine intelligence
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Particle swarm optimization
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Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing
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An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
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Benchmarking Least Squares Support Vector Machine Classifiers
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Is Combining Classifiers with Stacking Better than Selecting the Best One?
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On the problem of local minima in backpropagation
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Evolutionary ensembles with negative correlation learning
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An evolutionary algorithm that constructs recurrent neural networks
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A new evolutionary system for evolving artificial neural networks
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G-Prop-II: global optimization of multilayer perceptrons using GAs
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Particle swarm optimization
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Cooperative Coevolution of Artificial Neural Network Ensembles for Pattern Classification
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Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior
inferredcites →https://doi.org/10.1109/tevc.2009.2033580openalex-snapshot
Chemical-Reaction-Inspired Metaheuristic for Optimization
inferredcites →https://doi.org/10.1109/tnn.2002.804317openalex-snapshot
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
inferredcites →https://doi.org/10.1109/tnn.2003.813832openalex-snapshot
A constructive algorithm for training cooperative neural network ensembles
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Mutation-Based Genetic Neural Network
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Tuning the Structure and Parameters of a Neural Network by Using Hybrid Taguchi-Genetic Algorithm
inferredcites →https://doi.org/10.1109/tnn.2006.881047openalex-snapshot
An Optimization Methodology for Neural Network Weights and Architectures
inferredcites →https://doi.org/10.1109/tnn.2008.2000444openalex-snapshot
Hybrid Multiobjective Evolutionary Design for Artificial Neural Networks
inferredcites →https://doi.org/10.1109/tpds.2011.35openalex-snapshot
Chemical Reaction Optimization for Task Scheduling in Grid Computing
inferredcites →https://doi.org/10.1109/tsmcb.2005.847740openalex-snapshot
An Empirical Comparison of Combinations of Evolutionary Algorithms and Neural Networks for Classification Problems
inferredcites →https://doi.org/10.1126/science.220.4598.671openalex-snapshot
Optimization by Simulated Annealing
inferredcites →https://doi.org/10.1142/9789811205675_0004openalex-snapshot
Particle Swarm Optimization
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Evolving Neural Networks through Augmenting Topologies
inferredcites →https://doi.org/10.7551/mitpress/1090.001.0001openalex-snapshot
Adaptation in Natural and Artificial Systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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The Role of Artificial Neural Networks in Evolutionary Optimisation: A Review
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Chemical Reaction Optimization: A Survey with Application and Challenges
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