Mutation-Based Genetic Neural Network

Palmes, Paulito; Hayasaka, Taichi; Usui, Shiro

doi:10.1109/tnn.2005.844858

Cited by 144 publications

(50 citation statements)

References 40 publications

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“…The network is formed by interconnecting artificial neurons arranged in layers, simulating a biological neural network [24]. It is an adaptive system with the ability to learn from provided data.…”

Section: Artificial Neural Network Trainingmentioning

confidence: 99%

Chemical Reaction Optimization: a tutorial

2012

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Chemical Reaction Optimization (CRO) is a recently established metaheuristics for optimization, inspired by the nature of chemical reactions. A chemical reaction is a natural process of transforming the unstable substances to the stable ones. In microscopic view, a chemical reaction starts with some unstable molecules with excessive energy. The molecules interact with each other through a sequence of elementary reactions. At the end, they are converted to those with minimum energy to support their existence. This property is embedded in CRO to solve optimization problems. CRO can be applied to tackle problems in both the discrete and continuous domains. We have successfully exploited CRO to solve a broad range of engineering problems, including the quadratic assignment problem, neural network training, multimodal continuous problems, etc. The simulation results demonstrate that CRO has superior performance when compared with other existing optimization algorithms. This tutorial aims to assist the readers in implementing CRO to solve their problems. It also serves as a technical overview of the current development of CRO and provides potential future research directions.

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Section: Artificial Neural Network Trainingmentioning

confidence: 99%

Chemical Reaction Optimization: a tutorial

2012

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“…When presented with a completely new set of data, the capability to generalize is one of the most significant criteria to determine the effectiveness of artificial neural network learning. Table 7 compares the results obtained with that of MGNN [31] and NN-MOPSOCD [29] in terms of the error on the test set and the number of connections used. Table 7 shows that the CSONN-OBD is very effective in generating simple and accurate artificial neural networks with good generalization capability.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm

Yusiong¹

2012

IJISA

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Abstract-An Artificial Neural Network (ANN) is an abstract representation of the biological nervous system which has the ability to solve many complex problems. The interesting attributes it exhibits makes an ANN capable of -learning‖. ANN learning is achieved by training the neural network using a training algorithm. Aside from choosing a training algorithm to train ANNs, the ANN structure can also be optimized by applying certain pruning techniques to reduce network complexity. The Cat Swarm Optimization (CSO) algorithm, a swarm intelligence-based optimization algorithm mimics the behavior of cats, is used as the training algorithm and the Optimal Brain Damage (OBD) method as the pruning algorithm. This study suggests an approach to ANN training through the simultaneous optimization of the connection weights and ANN structure. Experiments performed on benchmark datasets taken from the UCI machine learning repository show that the proposed CSONN-OBD is an effective tool for training neural networks.

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“…The Evolutionary Algorithms (EA) are broadly classified into three categories. They are Evolutionary Strategies (ES), Evolutionary Programming (EP) and Genetic Algorithms (GA) (Palmes et al, 2005). Ingo Rechenberg andHans-Paul Schwefel (1960s andearly 1970s) solved complex engineering problems through artificial evolution strategies using optimization method.…”

Section: Introductionmentioning

confidence: 99%

VHDL Design and Hardware Realization of Hybridartificial Intelligence Architecture

Nagalingam¹

2014

American Journal of Applied Sciences

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Evolutionary Algorithms (EA) use Genetic Algorithm (GA) in many optimization problems to efficiently compute the function value in less time. In this study the weight optimization of the Artificial Neural Network (ANN), using the Back Propagation Network (BPN), is tested and presented with GA. The combined architecture of Neuro-Genetic (Hybrid Artificial Intelligence) approach is proposed and simulated results are provided along with device Utilization, Simulation time, Timing analysis and power analysis by using very high speed integrated circuits Hardware Description Language (HDL).

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Mutation-Based Genetic Neural Network

Cited by 144 publications

References 40 publications

Chemical Reaction Optimization: a tutorial

Chemical Reaction Optimization: a tutorial

Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm

VHDL Design and Hardware Realization of Hybridartificial Intelligence Architecture

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