Abstract
In this paper, we address the automated product design problem by two distinct evolutionary approaches: genetic algorithms and evolutionary ontologies. Based on the mechanisms and internal representation of each algorithm, their capabilities are different, which means that the structure and complexity of the products differs. We provide detailed description of the evolutionary ontologies: crossover, mutation, repair and selection operators. Finally, both approaches are tested, benchmarked and compared in the case of power train design.
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Acknowledgments
The research leading to these results has received funding from the European Community Seventh Framework Programme under Grant Agreement No. 609143 Project ProSEco. The authors are grateful to the anonymous referees for reading the manuscript very carefully and providing constructive comments which helped to improve substantially the paper.
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Authors Oliviu Matei, Diana Contraş, Petricǎ Pop and Honoriu Vǎlean declare that they have no conflict of interest.
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Communicated by A. Herrero.
This is a modified version of the paper “Relational Crossover in Evolutionary Ontologies” by Matei, Oliviu, Diana Contra, and Honoriu Vǎlean, published in 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Springer International Publishing, 2015.
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Matei, O., Contraş, D., Pop, P. et al. Design and comparison of two evolutionary approaches for automated product design. Soft Comput 20, 4257–4269 (2016). https://doi.org/10.1007/s00500-016-2292-x
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DOI: https://doi.org/10.1007/s00500-016-2292-x