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In this paper, we investigate the utilization of a multi-objective approach for evolving artificial neural networks (ANNs) that act as controllers for a radio frequency (RF) based collective box-pushing task of a group of virtual E-puck robots simulated in a 3D, physics-based environment. The modified Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal sets...
A number of studies have demonstrated the capability of ANNs for the required robot behaviors by using an evolutionary optimization technique in generating more complex robot controllers. Interestingly however, there is still a serious lack of research in exploring the application of Evolutionary Multi-objective Optimization (EMO) algorithm in evolutionary robotics. In this paper, we investigate the...
This paper proposes a multi-classification pattern algorithm using multilayer perceptron neural network models which try to boost two conflicting main objectives of a classifier, a high correct classification rate and a high classification rate for each class. To solve this machine learning problem, we consider a memetic Pareto evolutionary approach based on the NSGA2 algorithm (MPENSGA2), where we...
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...
This paper presents the evolutionary neural network (ENN) model for the prediction of output from a grid-connected photovoltaic system installed at Malaysian Energy Centre (PTM), Bangi, Malaysia. The ENN model had been developed using evolutionary programming (EP) through the optimization of the number of nodes in the hidden layer, the learning rate and the momentum rate. The ENN model employs solar...
This paper discussed the utilization of a multi-objective approach for evolving artificial neural networks (ANNs) that act as a controller for radio frequency (RF)-localization behavior of a virtual Khepera robot simulated in a 3D, physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal sets of ANNs that optimize the conflicting...
This paper presents the optimization of one-hidden layer artificial neural network (ANN) design using evolutionary programming (EP) for predicting the energy output of a grid-connected photovoltaic system installed at Malaysian Energy Centre (PTM), Bangi, Malaysia. In this study, the architecture and training parameters of the multi-layer feedforward back-propagation ANN model had been optimized while...
This article describes a simulation model in which a multi-objective approach is utilized for evolving an artificial neural networks (ANNs) controller for an autonomous mobile robot. A mobile robot is simulated in a 3D, physics-based environment for the RF-localization behavior. The elitist Pareto-frontier differential evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs that...
The main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR and sensitivity (given by the lowest percentage of examples correctly predicted to belong to each class). We present a memetic Pareto evolutionary NSGA2 (MPENSGA2) approach based on the...
In this study, we investigate the utilization of a multi-objective approach in evolving artificial neural networks (ANNs) for an autonomous mobile robot. The ANN acts as a controller for radio frequency (RF)-localization behavior of a Khepera robot simulated in a 3D physics-based environment. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal...
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