In recent years, it is required to reduce the amount of waste since there is a growing interest in environmental issues. Hence, it is important to reuse and recycle the used products efficiently. In this paper, we pro...
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We present an evolutionary algorithm to solve a combination of the Order Batching and Order Picking problems. This integrated problem consists of selecting and picking up batches of various items requested by customer...
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evolutionary multiobjective multitask optimization (EMTO) has attracted widespread attention in recent years, which solves multiple tasks simultaneously in a single population. How to extract effective knowledge and r...
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Diffusion tensor tractography can reconstruct white matter fiber bundle non-invasively. In this paper, a novel fiber tracking algorithm is proposed based on an adaption of evolutionary computation. Given a pair of ROI...
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This paper presents a hybrid method of fuzzy clustering using evolutionary search and self-organization, which has the advantages of both. Simulation experiments have shown that the convergence speed of the proposed m...
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Due to its ability to handle nonlinear functions regardless of the derivative information, evolutionary Programming (EP) are envisaged to be very effective for Maximum Power Point Tracking (MPPT) of Photovoltaic cell ...
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Recent research in Cooperative Coevolution (CC) have achieved promising progress in solving large-scale global optimization problems. However, existing CC paradigms have a primary limitation in that they require deep ...
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Previous work presented an approach based on Co-evolutionary Particle Swarm Optimization (Co-PSO) to solve constrained optimization problems formulated as min-max problems. Preliminary results demonstrated that Co-PSO...
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ISBN:
(纸本)0780385152
Previous work presented an approach based on Co-evolutionary Particle Swarm Optimization (Co-PSO) to solve constrained optimization problems formulated as min-max problems. Preliminary results demonstrated that Co-PSO constitutes a promising approach to solve constrained optimization problems. However the difficulty to obtain fine tuning of the solution using a uniform distribution became evident. In this paper, a modified PSO using a Gaussian distribution is applied in the context of Co-PSO. The new modified Co-PSO is tested on some benchmark optimization problems and the results show a superior performance compared to the standard Co-PSO.
Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automa...
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Neural networks have been used in many classification tasks. However, their success depends on determining their training algorithm and architecture, which is often a trial-and-error process. evolutionary algorithms h...
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Neural networks have been used in many classification tasks. However, their success depends on determining their training algorithm and architecture, which is often a trial-and-error process. evolutionary algorithms have been used to address these problems successfully in recent years. This paper reviews different combinations between the most widely used type of neural networks - a multi-layer perceptron - and evolutionary algorithms. Several methods to determine the architecture and train the weights of the network are tested using a real-world classification problems from Proben1 benchmark suite. It is shown, that combining evolutionary algorithms with neural networks can lead to better results than relying on neural networks alone. Comparison to gradient algorithms is discussed.
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