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12 Applications of Genetic Progrmming

12 Applications of Genetic Progrmming. AI Lab. 신수용. Contents. General Overview Application from A to Z Science-Oriented Applications of GP Computer-Oriented Applications Engineering-Oriented Applications of GP Summary. 12.1 General Overview. Development of GP literature since 1989.

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12 Applications of Genetic Progrmming

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  1. 12 Applications of Genetic Progrmming AI Lab. 신수용

  2. Contents • General Overview • Application from A to Z • Science-Oriented Applications of GP • Computer-Oriented Applications • Engineering-Oriented Applications of GP • Summary

  3. 12.1 General Overview • Development of GP literature since 1989

  4. 12.2 Applications from A to Z (1)

  5. 12.2 Applications from A to Z (2)

  6. 12.2 Applications from A to Z (3)

  7. 12.2 Applications from A to Z (4)

  8. 12.2 Applications from A to Z (5)

  9. 12.3 Science-Oriented Application of GP12.3.1 Biochemistry Data Mining • biochemistry database CONSOLV • K-nearest neighbors + GP system • Raymer et al. 1996 • 시스템 구성도

  10. 12.3.1 Biochemistry Data Mining (2) • GP elements • function sets : +. -. *, % • terminal sets : original features • P = 100 • Gmax = 300 • Pc = 0.9 • MDP = 17 • MDPinit = 6 • computing time / generation = 15 min (on SUN SPARC 502)

  11. 12.3.2 Sequence Problems • Sequence problems • Speech processing • Communication in general • Language understanding and translation • analysis of economic problems • DNA pattern recognition • Time series analysis: prediction of weather, etc. • Secondary and tertiary protein structure prediction • Handley 1996 • 122 proteins from the Brookhaven Protein Data Bank

  12. 12.3.3 Image Classification in Geoscience and Remote Sensing • Daida et al. 1996 • GP- supported image processing system for the analysis of satellite radar images in a geoscience application • 목적 : to find an automatic algorithm that can extract these diffuse features directly from satellite images

  13. 12.4 Computer Science-Oriented Application12.4.1 Cellular Encoding of ANN • Grauau 1992-1995 • automatic generation of neural networks • six-legged insect 움직임 제어 • task : to coordinate the different neurons on different legs so as to end up with coordinated motion in various gaits

  14. 12.4.1 Cellular Encoding of ANN

  15. 12.4.2 Development and Evolution of Hardware Behaviors • Hemmi et al. 1994 • circuit synthesis

  16. 12.4.3 Intrusion Detection • Crosbie et al. 1995 • computer system defense

  17. 12.4.4 Autoparallelization • Walsh et al. 1996 • software engineering • PARAGEN - a GP-based system for autoparallelization of serial software • pop size = 100, gen = 10

  18. 12.4.5 Confidence of Text Classification • Brij Basand 1994 • GP to the evolution of such confidence values for automatically classified news stories • fitness : recall, precision, proportion of texts accepted for automatic classification • terminal sets : scores of the assigned codes and five numerical constants(1-5) • function sets : _, - , X, /, square-root • GP system beat hand-constructed formulas by about 14%

  19. 12.3.6 Image Classification with the PADO system • Teller and Veloso 1996 PADO system • classification of images and sound • evolutionary program-induction system • 2800 individual, 80 gen

  20. 12.5 Engineering-Oriented Application of GP12.5.1 Online Control of Real Robot • 시뮬레이션이 주류 • 실제 Robot에 구현을 하기엔 많은 문제점이 존재 • 학습 시간이 너무 오래 소요됨 • Banzhaf et al. 1995-1997 • AIMGP 개발 • obstacle-avoiding behavior using 8 sensors • khepera platform

  21. 12.5.1 Online Control of Real Robot • 시스템 개요도

  22. 12.5.2 Spacecraft Attitude Maneuvers • Howley 1996 • optimal control for spacecraft • find a control program that performs a 3D craft reorientation in minimal time

  23. 12.5.3 Hexapodal Robot • Spencer 1994 • GP to robotics • simulated six-legged robot is to be controlled by GP • Interesting possibility • real-time GP control of robotic systems • 변화하는 환경에 어떻게 적응하는지가 문제

  24. 12.5.4 Design of Electrical Circuits • Automatic design of electrical circuits • Koza et al. 1996 • GP successfully to evolve a large number of different circuits with good results • pop size = 640,000 gen = 200 • possible to solve problems that are regarded as difficult for a human designer

  25. 12.5.5 Articulated Figure Motion Animation • Gritz and Hahn 1995 • GP for animating figures • 시스템 구성도 • computation time / gen = a few min. on MIPS R4000 • good when compared to that of a human animator drawing a sequence of frames by hand

  26. 12.6 Summary • GP has shown its worth in a broad spectrum of real-life problem domains with remarkable flexibility as a machine learning technique • GP uses generally very robust evolutionary search • GP 발전 가능성은 무한

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