Abstract
Environmental impact assessment of products has been a key area of research and development for sustainable product development. Many companies copy these trends and they consider environmental criteria into the product design process. Life Cycle Assessment (LCA) is used to support the decision-making for product design and the best alternative can be selected by its estimated environmental impacts and benefits. The need for analytical LCA has resulted in the development of approximate LCA. This paper presents an optimization strategy for approximate LCA using a hybrid approach which incorporate genetic algorithms (GAs) and neural networks (NNs). In this study, GAs are employed to select feature subsets to eliminate irrelevant factors and determine the number of hidden nodes and processing elements. In addition, GAs will optimize the connection weights between layers of NN simultaneously. Experimental results show that a hybrid GA and NN approach outperforms the conventional backpropagation neural network and verify the effectiveness of the proposed approach.
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Seo, KK., Kim, WK. (2007). Approximate Life Cycle Assessment of Product Concepts Using a Hybrid Genetic Algorithm and Neural Network Approach. In: Szczuka, M.S., et al. Advances in Hybrid Information Technology. ICHIT 2006. Lecture Notes in Computer Science(), vol 4413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77368-9_26
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DOI: https://doi.org/10.1007/978-3-540-77368-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77367-2
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