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Evaluation of Sequential, Multi-objective, and Parallel Interactive Genetic Algorithms for Multi-objective Floor Plan Optimisation

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3907)

Abstract

We propose a sequential IGA, multi-objective IGA and parallel interactive genetic algorithm (IGA), and evaluate them with a multi-objective floor planning task through both simulation and real IGA users. Combining human evaluation with an optimization system for engineering design enables us to embed domain specific knowledge which is frequently hard to describe, subjective criteria and preferences in engineering design. We introduce IGA technique to extend previous approaches with sequential single objective GA and multi-objective GA. We also introduce parallel IGA newly. Experimental results show that (1) the multi-objective IGA and the parallel IGA clearly provide better results than the sequential IGA, and (2) the multi-objective IGA provides more diverse results and faster convergence for a floor planning task although the parallel IGA provides better fitness convergence.

Keywords

  • Pareto Front
  • Parallel Genetic Algorithm
  • Quantitative Fitness
  • Interactive Genetic Algorithm
  • Interactive Evolutionary Computation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2006 Springer-Verlag Berlin Heidelberg

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Brintrup, A.M., Takagi, H., Ramsden, J. (2006). Evaluation of Sequential, Multi-objective, and Parallel Interactive Genetic Algorithms for Multi-objective Floor Plan Optimisation. In: , et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_56

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  • DOI: https://doi.org/10.1007/11732242_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33237-4

  • Online ISBN: 978-3-540-33238-1

  • eBook Packages: Computer ScienceComputer Science (R0)