Hybrid Genetic Algorithm Model in Neuro Symbolic Integration

The development of artificial neural network and logic programming plays an important part in neural network studies. Genetic Algorithm (GA) is one of the escorted randomly searching technicality that uses evolutional concepts of the natural election as a stimulus to solve the computational problems. The essential purposes behind the studies of the evolutional system is for developing adaptive search techniques which are robust. In this paper, GA is merged with agent based modeling (ABM) by using specified proceedings to optimise the states of neurons and energy function in the Hopfield neural network (HNN). Hence, it is observed that the GA provides the best solutions in affirming optimal states of neurons and thus, enhancing the performance of Horn Satisfiability logical program (HornSAT) in Hopfield neural network. This is due to the fact that the GA lesser susceptive to be restricted in the local optimal or in any suboptimal solutions. NETLOGO version 6.0 will be used as a dynamic platform to test our proposed model. Hence, the computer simulations will be carried out to substantiate and authenticate the efficiency of the proposed model. The results are then tabulated by evaluating the global minimum ratio, computational time and hamming distance

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Optimal Generation Dispatch of Ethiopian Power System Using Hybrid Genetic Algorithm-Hopfield Neural Network

EAI Endorsed Transactions on Energy Web ◽

10.4108/eai.13-8-2021.170673 ◽

2021 ◽

pp. 170673

Author(s):

Shewit Tsegaye ◽

Getachew Bekele

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Power System ◽

Hybrid Genetic Algorithm ◽

Hopfield Neural Network ◽

Generation Dispatch

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Optimization Design of Structure and Parameters for RBF Neural Network Using Hybrid Hierarchy Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.951.274 ◽

2014 ◽

Vol 951 ◽

pp. 274-277 ◽

Cited By ~ 2

Author(s):

Xu Sheng Gan ◽

Can Yang ◽

Hai Long Gao

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Optimization Design ◽

Rbf Neural Network ◽

Hybrid Genetic Algorithm ◽

Proposed Model ◽

Crossover Probability ◽

Ga Algorithm ◽

The Hierarchical Structure ◽

Adaptive Crossover

To improve the optimization design of Radial Basis Function (RBF) neural network, a RBF neural network based on a hybrid Genetic Algorithm (GA) is proposed. First the hierarchical structure and adaptive crossover probability is introduced into the traditional GA algorithm for the improvement, and then the hybrid GA algorithm is used to optimize the structure and parameters of the network. The simulation indicates that the proposed model has a good modeling performance.

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Discrete Artificial Dragonflies Algorithm in Agent Based Modelling for Exact Boolean kSatisfiability Problem

Journal of Advances in Mathematics and Computer Science ◽

10.9734/jamcs/2020/v35i430275 ◽

2020 ◽

pp. 115-134

Author(s):

Hamza Abubakar ◽

Sagir Abdu M. ◽

Surajo Yusuf ◽

Yusuf Abdurrahman

Keyword(s):

Hamming Distance ◽

Search Space ◽

Boolean Satisfiability ◽

Computational Time ◽

Discrete Version ◽

Agent Based ◽

Dragonfly Algorithm ◽

Agent Based Modelling ◽

Boolean Satisfiability Problem ◽

Minimum Ratio

The development of metaheuristics and Boolean Satisfiability representation plays an important part in a neural network (NN) and Artificial Intelligence (AI) communities. In this paper, a new hybrid discrete version of the artificial dragonfly algorithm (DADA) applying a minimum objective function in agent-based modelling (ABM) obeying a specified procedure to optimize the states of neurons, for optimal Boolean Exact Satisfiability representation on NETLOGO as a dynamic platform. We combined the artificial dragonfly algorithm for its random searching ability that encourages diverse solutions and formation of static swarm’s mechanism to stimulus computational problems to converge to the best global optimal search space. The global performance of the proposed DADA was compared with genetic algorithm (GA) that are available in the literature based on the global minimum ratio (gM), Local Minimum Ratio (yM), Computational time (CPU) and Hamming distance (HD). The final results showed good agreement between the proposed DADA and discrete version of GA to efficiently optimize the Exact-kSAT problem. It found that DADA-ABM has high potentiality for optimizing or modelling a network that is very hard or often impossible to capture by exact or traditional optimization modelling techniques such as Boolean satisfiability problem is better than existing methods in the literature.

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Agent-based Modeling for Activation Function in Enhancement Logic Programming in Hopfield Neural Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d7711.049420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1872-1879

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logic Programming ◽

Hamming Distance ◽

Hopfield Neural Network ◽

Activation Function ◽

Agent Based Modeling ◽

Activation Functions ◽

Agent Based ◽

Artificial Neural

Artificial Neural Network (ANN) uses many activation functions to update the state on neuron. The research and engineering have been used activation functions in the artificial neural network as the transfer functions. The most common reasons for using this transfer function were its unit interval boundaries, the functions and quick computability of its derivative, and several useful mathematical properties in the approximation of theory realm. Aim of this study is to figure out the best robust activation functions to accelerate HornSAT logic in the Hopfield Neural Network's context. In this paper we had developed Agent-based Modelling (ABM) assessed the performance of the Zeng Martinez Activation Function (ZMAF) and the Hyperbolic Tangent Activation Function (HTAF) beside the Wan Abdullah method to do Logic Programming (LP) in Hopfield Neural Network (HNN). These assessments are carried out on the basis of hamming distance (HD), the global minima ratio (zM), and CPU time. NETLOGO 5.3.1 software has been used for developing Agent-based Modeling (ABM) to test the proposed comparison of the efficaecy of these two activation functions HTAF and ZMAF.

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