Overview of IGA Application and Combating User Fatigue

1. Overview of IGA Application and Combating User Fatigue Jie-Wei Wu 2013/3/12

2. Agenda • Different Applications Using IGA • S.-B. Cho and J.-Y. Lee, “A human-oriented image retrieval system using interactive genetic algorithm,” IEEE Trans. Syst., Man, Cybern. A, vol. 32, pp. 452–458, May 1998. • Kim, H.-S. and Cho, S.-B. (2000), “Application of interactive generic algorithm to fashion design”, Engineering Applications of Artificial Intelligence, Vol. 13, pp. 635-44.

3. Agenda • Llorà, X., Sastry, K., Goldberg, D. E., Gupta, A., and Lakshmi, L. (2005). Combating user fatigue in iGAs: Partial ordering, support vector ma-chines, and synthetic fitness. Proceedings of the Genetic and Evolutionary Computation Conference, pages 1363-1370.

4. Agenda • Different Applications Using IGA • Kim, H.-S. and Cho, S.-B. (2000), “Application of interactive generic algorithm to fashion design”, Engineering Applications of Artificial Intelligence, Vol. 13, pp. 635-44. • S.-B. Cho and J.-Y. Lee, “A human-oriented image retrieval system using interactive genetic algorithm,” IEEE Trans. Syst., Man, Cybern. A, vol. 32, pp. 452–458, May 1998.

5. Motivation • As most consumers are not professional at design, however, a sophisticated computer-aided design system might be helpful to choose and order what they want.

10. Experimental results • Population size is 8. • The number of generation is limited to 10. • One-point XO • One elitist individual in each generation • Convergence test and subject test

11. Convergence Test • 10 subjects are requested to find cool-looking design and splendid design using the system. • The meaning of ‘splendid’ might be more complex and various than that of ‘cool-looking.’

12. Subject Test • Randomly selected 500 sample designs from entire search space, and requested 3 subjects to evaluate the sample designs with two categories, coolness and splendor. • 10 most cool-looking designs and another 10 most splendid ones are selected as standards of evaluation.

13. Subject Test • Request 10 subjects to find cool-looking design and splendid design using the system. • Each searching is limited to 10 generations.

14. Subject Test • This score has 7 degrees (from -3 to 3).

15. Conclusion of This Paper • An IGA-based fashion design aid system for non-professionals. • A more realistic and reasonable design in OpenGL design model. • Future :the search space should be enlarged.(original 1,880,064)

16. Different Applications Using IGA • Kim, H.-S. and Cho, S.-B. (2000), “Application of interactive generic algorithm to fashion design”, Engineering Applications of Artificial Intelligence, Vol. 13, pp. 635-44. • S.-B. Cho and J.-Y. Lee, “A human-oriented image retrieval system using interactive genetic algorithm,” IEEE Trans. Syst., Man, Cybern. A, vol. 32, pp. 452–458, May 1998.

17. Motivation • Most of the conventional methods lack of the capability to utilize human intuition and emotion appropriately in the process of retrieval. • It is difficult to retrieve a satisfactory result when the user wants an image that cannot be explicitly specified because it deals with emotion.

18. Discrete Wavelet Transform • Construct a matrix of coefficient values. • HaarWavelet Transform • Only the largest 50 coefficients in RGB channels.

19. GA Operators • Population size is 12. • Horizontal and vertical crossovers

21. Search • The similarity between potential target image and candidate image is calculated. • 12 images of higher magnitude value are provided as a result of the search.

22. Experimental Results • Convergence Test • Efficiency Test • Psychological Test

23. Convergence Test • “We can see that there are more images of gloomy mood in the eighth generation than those in the beginning.” ?

24. Efficiency Test • Request 10 graduate students to search gloomy and splendid images and ask how similar the result image is to what they have in mind.

25. Psychological Test • 3 subjects, 2 motif(gloomyand cheerful), manually select 8 images in 500 images from db. • 10 subjects to search images with the same motives using the proposed system. • The number of paired images: = 36

26. Psychological Test • The number of paired images: = 36 • “ 10 subjects give seven step scores to the difference between a pair of images considering the given motif. “ ?

27. Conclusion of This Paper • An approach that searches an image with human preference and emotion using GA. • To search not only an explicitly expressed image, but also an abstract image such as “cheerful impression image,” “gloomy impression image,” and so on.

28. Combating User Fatigue in iGAs: Partial ordering, Support Vector Machines, and Synthetic Fitness Llorà, X., Sastry, K., Goldberg, D. E., Gupta, A., and Lakshmi, L. (2005).Proceedings of the Genetic and Evolutionary Computation Conference, pages 1363-1370.

29. Motivation • One of the daunting challenges of interactive genetic algorithms (iGAs) is user fatigue which leads to sub-optimal solutions. • Combating the user fatigue problem of iGAs: • The lack of a computable fitness • How synthetic fitness models based on user evaluation may be built.

30. Components of Proposed Method • Partial Ordering: The qualitative decisions made by the user about relative solution quality is used to generate partial ordering of solutions • Induced Complete Order: The concepts of non-domination and domination count from multi-objective evolutionary algorithms to induce a complete order of the solutions in the population based on their partial ordering • Surrogate Function via SVM: The induced order is used to assign ranks to the solutions and use them in a support vector machine (SVM) to create a surrogate fitness function that effectively models user fitness.

31. Elements IGAs Need to Address • Clear goal definition • Impact of problem visualization • Lack of real fitness • Fatigue • Persistence of user criteria

32. Synthetic Fitness • Properties • Fitness extrapolation: it requires that the synthetic fitness provide meaningful inferences beyond the boundaries of the current partial order provided by the user. • Order maintenance: it guarantees that a synthetic fitness is accurate if it maintains the partial ordering given by the user decisions.

33. Surrogate Models • Models need to satisfy the above requirements. • ε-SVM using a linear kernel

34. ε-SVM • Using a linear kernel easily satisfies the fitness extrapolation and order maintenance propertiesImpact of problem visualization. • Hyper-plane adjustment • Even with a high-regression error, a ε-SVM guarantees the proper ordering of solutions under a tournament selection scheme.

35. Synthesis • The surrogate models make a basic assumption: the partial order of user evaluations can be translated into a global numeric value.

36. Synthesis

37. Synthesis • δ(v) as the number of different nodes present on the paths departing from vertex v. • φ(v) is defined as the number of different nodes present on the paths arriving to v.

38. Synthesis • Estimated ranking may be used to train a ε-SVM.

39. Active Interactive Genetic Algorithms • The compact GA is a suitable option to optimize the synthetic fitness • Initialization: random individuals, probabilities are initially set to 0.5 • Model sampling: generate two candidate solutions by sampling the probability vector. The model sampling procedure is equivalent to uniform crossover in simple GAs.

40. Active Interactive Genetic Algorithms • Selection: tournament selection • Probabilistic model updating

41. Active Interactive Genetic Algorithms

42. Active Interactive Genetic Algorithms • Population-Sizing Model and Convergence-Time Model: approximate form of the cGA is operationally equivalent to the order-one behavior of simple genetic algorithm with steady state selection and uniform crossover.

43. Experimental Results and Analysis • Population-Sizing Model: approximate form of the gambler’s ruin population-sizing model • Convergence-Time Model: approximate form of Miller and Goldberg’s convergence-time model

44. Experimental Results and Analysis • The number of function evaluations required for successful convergence, of GAs as follows:

45. Experimental Results and Analysis • IGA requires a population size that grows linearly. It’s the result of using a ε-SVM.

46. Experimental Results and Analysis • The active IGA population is also constrained by the three tournament structure. • The population size is forced to grow

47. Experimental Results and Analysis • The theoretical convergence time of the active iGA with the proper population sizing, should be constant.

48. Experimental Results and Analysis • A simple low-cost high-error synthetic fitness function we were able to achieve speedups ranging from 3 up to 7 times.

49. Conclusion of this Paper • Propose a heuristic to synthesize a model of the user fitness combining partial ordering concepts, multiobjectiveoptimization ideas, and support vector machines. • Model provided by a ε-SVM is able to satisfy the two properties a synthetic fitness need to satisfy—fitness extrapolation, and order maintenance.

50. Conclusion of this Paper • The existence of a synthetic fitness allow us to actively use such model to combat user fatigue. • The injection of such candidate solutions into the user evaluation process effectively reduce the number of evaluations required on the user side till convergence.