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An Evolutionary Algorithm with Species-specific Explosion for Multimodal Optimization

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javan
25 diapositives · 401 Views · Sep 12, 2014

This paper presents an innovative evolutionary algorithm called Evolutionary Algorithm with Species-specific Explosion (EASE) aimed at improving multimodal optimization. Unlike traditional approaches that seek a single global optimum, EASE targets all optimal points in a single run by employing species seeds for convergence. By implementing species-specific explosion, multiple mutated copies of species seeds are generated to enhance diversity and facilitate the search for both local and global optima. Experimental results indicate that EASE outperforms existing algorithms in benchmark tests, showcasing its potential in optimization tasks.

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An Evolutionary Algorithm with Species-specific Explosion for Multimodal Optimization
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  1. An Evolutionary Algorithm with Species-specific Explosion for Multimodal Optimization GECCO 2009 Ka-Chun Wong, Kwong-Sak Leung, Man-Hon Wong Department of Computer Science & Engineering The Chinese University of Hong Kong, Hong Kong {kcwong, ksleung, mhwong}@cse.cuhk.edu.hk

  2. Who am I ? • Name: Wong Ka Chun (黃家駿) • Position: M.Phil. Student (yr1) in The Chinese University of Hong Kong (CUHK) • Biography • Ricky received his B.Eng. in Computer Engineering from United College, the Chinese University of Hong Kong in July 2008. Since August 2008, he has been a M.Phil. student at the Department of Computer Science and Engineering, the Chinese University of Hong Kong, under the co-supervision of Professor LEUNG Kwong-Sak and Professor WONG Man-Hon.With exposures to different aspects (academic, industrial, spiritual and social), he hope that he can strike a balance between them and contribute to the society by researches. • Research Interests • Evolutionary Algorithms • Bioinformatics • Geographical Information System • My Website: • http://www.cse.cuhk.edu.hk/~kcwong/

  3. Motivation • Given an optimization problem, traditional optimization algorithms can be applied to obtain the global optimum. • However, in the real world, we are often interested in not only a single global optimum, but also other possible global and local optima.

  4. Problem Definition • Given a function, an algorithm should work out all optimal points in a single run. Six-hump Camel Back Function (http://www.it.lut.fi)

  5. Previous works • AEGA (Leung et al. 2003) • SCGA (Li et al. 2002) • Crowding (Kenneth De Jong 1975) • Fitness Sharing (Goldberg et al. 1989) • CrowdingDE (R. Thomsen 2004) • SDE (Xiaodong Li 2005) • Repeated iterations (Beasley et al. 1993) • ……

  6. Species-Conserving Genetic Algorithm (SCGA) • Select species seeds • Each species is a subset of population. • The fittest individual within a species is chosen as the species seed. The region around a species seed forms its corresponding species region.

  7. Species-Conserving Genetic Algorithm • Main idea • Species seeds can bypass the genetic operations

  8. Species-Conserving Genetic Algorithm • Observation: • For each area (species), only one elitist (species seed) is saved. • Is it significant enough to help each species to converge to its respective optimum?

  9. Not enough individuals to coverge in this species! Species-Conserving Genetic Algorithm

  10. Proposed Method • Evolutionary Algorithm with Species-specific Explosion (EASE) • An extension of SCGA

  11. Proposed Method (EASE) • Main idea: • To exploit species seeds for convergences

  12. Proposed Method (EASE) • Species-specific explosion • The local operation in which we create multiple copies for each species seed and mutate them. http://www.japanairsoftguns.com/store/images/products/mkkt2shotgun.jpg http://en.wikipedia.org/wiki/File:Shotgun-shot-sequence-1g.jpg http://pixal.us/img/up/index/1271575147.jpg http://www.japanairsoftguns.com/store/images/products/mkkt2shotgun.jpg

  13. Proposed Method (EASE) • Species-specific explosion

  14. Proposed Method (EASE) • Species-specific explosion • Two parameters • Number of mutated copies • The step-size for mutation

  15. Proposed Method (EASE) • To determine number of mutated copies • No. of mutated copies = pop_size*K*weight • where K is a constant (similar to generation gap) No. of mutated copies = No. of yellow individuals

  16. Proposed Method (EASE) • To determine the step-size for mutation • Use the last known improvement step size. step-size for mutation = the average distance from the species seed (blue individual)

  17. Experiments • All algorithms were run up to maximum 50000 fitness function evaluations. The performance measurements are obtained by taking the average and standard deviation of 30 runs. • The parameter settings and results of some algorithms* are taken from: • R. I. Lung, C. Chira, and D. Dumitrescu. An agent-based collaborative evolutionary model for multimodal optimization. In GECCO ’08: Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation, pages 1969–1976, New York, NY, USA, 2008. ACM. • R. I. Lung and D. Dumitrescu. A new evolutionary model for detecting multiple optima. In GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1296–1303, New York, NY, USA, 2007. ACM. * The algorithms include: RACE, RO, AEGA, CRDE

  18. Experiments • Performance measurement

  19. Experiments

  20. Experiments

  21. Conclusion • The experimental results reveal that EASE can provide better performance than the others over the benchmark functions. However, it should not be taken to mean that the proposed method (EASE) is “better” than other evolutionary algorithms for multimodal optimization. Such a conclusion is oversimplified. • It shows that EASE improves SCGA for locating optima (global and local), in terms of peak ratio and accuracy without the addition of manual parameters

  22. Future works • Better methods to adaptively select • Number of mutated copies • The step-size for mutation • The concept Species-specific Explosion will be investigated to improve other evolutionary algorithms. • Further experiments will be conducted on high dimensional problems.

  23. Thank you!謝謝!

  24. Q&A

  25. Roaming Agent-Based Collaborative Evolutionary Model (RACE) • ROAMING OPTIMIZATION(RO)