Memetic Elitist Pareto Differential Evolution algorithm based Radial Basis Function Networks for classification problems (original) (raw)
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Multi-Objective Hybrid Evolutionary Algorithms for Radial Basis Function Neural Network Design
2011
This paper presents new multi-objective evolutionary hybrid algorithms for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithms are memetic Pareto particle swarm optimization based RBFN (MPPSON), Memetic Elitist Pareto non dominated sorting genetic algorithm based RBFN (MEPGAN) and Memetic Elitist Pareto non dominated sorting differential evolution based RBFN (MEPDEN). The proposed methods integrate accuracy and structure of RBFN simultaneously.
This paper presents a new multiobjective evolutionary algorithm applied to a radial basis function (RBF) network design based on mult iobjective particle swarm optimization augmented with local search features. The algorithm is named the memetic multiobjective particle swarm optimization RBF network (MPSON) because it integrates the accuracy and structure of an RBF network. The proposed algorithm is impleme nted on two-clas s and multiclass pattern classification problems with one complex real problem. The experimental results indicate that the proposed algorithm is viable, and provides an effe ctive means to design multiobjective RBF networks with good generalization capability and compact network structure. The accuracy and complexity of the network obtained by the proposed algorithm are comp ared with the memetic non-dominated sorting genetic algorithm based RBF netwo rk (MGAN) through statistical tests. This study shows that MPSON generates RBF networks coming with an appropriate balance between accuracy and simplicity, outperforming the other algorithms considered.
Evolutionary system for automatically constructing and adapting radial basis function networks
Neurocomputing, 2006
This article presents a new system for automatically constructing and training radial basis function networks based on original evolutionary computing methods. This system, called Genetic Algorithm Radial Basis Function Networks (GARBFN), is based on two cooperating genetic algorithms. The first algorithm uses a new binary coding, called basic architecture coding, to get the neural architecture that best solves the problem. The second, which uses real coding, takes its inspiration from mathematical morphology theory and trains the architectures output by the binary genetic algorithm. This system has been applied to a laboratory problem and to breast cancer diagnosis. The results of these evaluations show that the overall performance of GARBFN is better than other related approaches, whether or not they are based on evolutionary techniques.
Classification by Evolutionary Generalized Radial Basis Functions
This paper proposes a novelty neural network model by using generalized kernel functions for the hidden layer of a feed forward network (Generalized Radial Basis Functions, GRBF), where the architecture, weights and node typology are learned through an evolutionary programming algorithm. This new kind of model is compared with the corresponding models with standard hidden nodes: Product Unit Neural Networks (PUNN), Multilayer Perceptrons (MLP) and the RBF neural networks. The methodology proposed is tested using six benchmark classification datasets from well-known machine learning problems. Generalized basis functions are found to present a better performance than the other standard basis functions for the task of classification.
Radial basis function Network based on multi-objective particle swarm optimization
2009
The problem of unsupervised and supervised learning is discussed within the context of multi-objective optimization. In this paper, an evolutionary multi-objective selection method of RBF networks structure is discussed. The candidates of RBF network structure are encoded into the particles in PSO. Then, they evolve toward Pareto-optimal front defined by several objective functions concerning with model accuracy and model complexity. This study suggests an approach of RBF network training through simultaneous optimization of architectures and weights with PSO-based multi-objective algorithm. Our goal is to determine whether multi-objective PSO can train RBF networks, and the performance is validated on accuracy and complexity. The experiments are conducted on benchmark datasets obtained from the UCI machine learning repository. The results show that our proposed method provides an effective means for training RBF networks that is competitive with other evolutionary computational-based methods.
2009
In this paper, an adaptive evolutionary multi-objective selection method of RBF Networks structure is discussed. The candidates of RBF Network structures are encoded into particles in Particle Swarm Optimization (PSO). These particles evolve toward Pareto-optimal front defined by several objective functions with model accuracy and complexity. The problem of unsupervised and supervised learning is discussed with Adaptive Multi-Objective PSO (AMOPSO). This study suggests an approach of RBF Network training through simultaneous optimization of architectures and weights with Adaptive PSO-based multi-objective algorithm. Our goal is to determine whether Adaptive Multi-objective PSO can train RBF Networks, and the performance is validated on accuracy and complexity. The experiments are conducted on two benchmark datasets obtained from the machine learning repository. The results show that our proposed method provides an effective means for training RBF Networks that is competitive with PSO-based multi-objective algorithm.
Classification by evolutionary generalised radial basis functions
International Journal of Hybrid Intelligent Systems, 2010
This paper proposes a Neural Network model using Generalised kernel functions for the hidden layer of a feed forward network. These functions are Generalised Radial Basis Functions (GRBF), and the architecture, weights and node topology are learned through an evolutionary algorithm. The proposed model is compared with the corresponding standard hidden-node models: Product Unit (PU) neural networks, Multilayer Perceptrons (MLP) with Sigmoidal Units (SUs) and the RBF neural networks. The proposed methodology is tested using twelve benchmark classification datasets from well-known machine learning problems. GRBFs are found to perform better than other standard basis functions at the classification task.
Hierarchical Rank Density Genetic Algorithm for Radial-Basis Function Neural Network Design
International Journal of Computational Intelligence and Applications, 2003
In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A Hierarchical Rank Density Genetic Algorithm (HRDGA) is used to evolve the neural network's topology and parameters simultaneously. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies highlighted in literature. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to deal with the confliction between the training performance and network complexity. Instead of producing a single optimal solution, HRDGA provides a set of near-optimal neural networks to the designers so that they can have more flexibility for the final decision-making based on certain preferences. In terms of searching for a near-complete set of candidate networks with high performances, the networks designed by the proposed algorit...
1998
Neuro-fuzzy systems based on Radial Basis Function Networks (RBFN) and other hybrid artificial intelligence techniques are currently under intensive investigation. This paper presents a RBFN training algorithm based on evolutionary programming and cooperative evolution. The algorithm alternatively applies basis function adaptation and backpropagation training until a satisfactory error is achieved. The basis functions are adjusted through an error goal function obtained through training and testing of the second part of the network. The algorithm is tested on bench-mark data sets. It is applicable to on-line adaptation of RBFN and building adaptive intelligent systems ¦ § © § ©