Sequential metamodelling with genetic programming and particle swarms | IEEE Conference Publication | IEEE Xplore

Sequential metamodelling with genetic programming and particle swarms


Abstract:

This article presents an application of two main component methodologies of evolutionary algorithms in simulation-based metamodelling. We present an evolutionary framewor...Show More

Abstract:

This article presents an application of two main component methodologies of evolutionary algorithms in simulation-based metamodelling. We present an evolutionary framework for constructing analytical metamodels and apply it to simulations of manufacturing lines with buffer allocation problem. In this framework, a particle swarm algorithm is integrated to genetic programming to perform symbolic regression of the problem. The sampling data is sequentially generated by the particle swarm algorithm, while genetic programming evolves symbolic functions of the domain. The results are promising in terms of efficiency in design of experiments and accuracy in global metamodelling.
Date of Conference: 13-16 December 2009
Date Added to IEEE Xplore: 11 March 2010
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Conference Location: Austin, TX, USA

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