1 / 20

The Pareto fitness genetic algorithm: Test function study

The Pareto fitness genetic algorithm: Test function study. Wei-Ming Chen 2011.11.03. Outline. The Pareto fitness genetic algorithm (PFGA ) Experimental results Performance measures Conclusion. PFGA. Double ranking strategy (DRS )

sloan
Download Presentation

The Pareto fitness genetic algorithm: Test function study

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Pareto fitness genetic algorithm: Test function study Wei-Ming Chen 2011.11.03

  2. Outline • The Pareto fitness genetic algorithm (PFGA) • Experimental results • Performance measures • Conclusion

  3. PFGA • Double ranking strategy (DRS) • R’(i) : how many j that solution j performs better than solution i • the DRS of solution i :

  4. PFGA

  5. PFGA • Population size adaptive density estimation (PADE) • The cell width on i-th dimension Wdi • Wi : the width of the non-inferior cell

  6. PFGA • Each dimension : pieces • Total : near N pieces

  7. PFGA • Fitness function :

  8. PFGA • Selection operation • “binary stochastic sampling without replacement” • Normalizing the fitness of each considered individual by dividing it by the total fitness • Generate R1 => find which individual is there • Generate R2 => find another individual

  9. PFGA • Elitist external set : the set of non-dominated individuals • updated at each generation

  10. FPGA

  11. Experimental results

  12. Experimental results

  13. Experimental results

  14. Experimental results

  15. Experimental results

  16. Experimental results

  17. Performance measures • some quantitative measures are used to evaluate the trade-off surface fronts (E. Zitzler, K. Deb, L. Thiele, Comparison of multi-objective evolutionary algorithms) • The convergence to the Pareto optimal front. • The distribution and the number of non-dominated solutions found. • The spread of the given set.

  18. Performance measures

  19. Conclusion • A new MOEA design was proposed in this paper!! • a modified ranking strategy, a promising sharing procedure and a new fitness function design • a relatively good performance when dealing with different Pareto front features

  20. Conclusion • Although the MOEA comparison may be useful, we think that the aim of the multi-objective optimization is not to decide which algorithm outperforms the other but how to deal with difficult problems, which genetic operator may be more suitable for which algorithm to solve a given kind of problems, how to extract the best features from the existing approaches and why not to hybridize some of them to provide better problems’ solutions.

More Related