1 / 48

Interactive Evolutionary Computation: A Framework and Applications

Interactive Evolutionary Computation: A Framework and Applications. November 10, 2009 Sung-Bae Cho Dept. of Computer Science Yonsei University. Agenda. Overview Methodology IGA Knowledge-based encoding Partial evaluation based on clustering

Download Presentation

Interactive Evolutionary Computation: A Framework and Applications

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. Interactive Evolutionary Computation:A Framework and Applications November 10, 2009 Sung-Bae Cho Dept. of Computer Science Yonsei University

  2. Agenda • Overview • Methodology • IGA • Knowledge-based encoding • Partial evaluation based on clustering • Direct manipulation of evolution with NK-landscape model • Applications • Media retrieval • Media design • Concluding remarks

  3. Overview Interactive Computational Intelligence Interaction/Evolution Human Computer Interface User Modeling (Efficiency) (Emotion) AI (Soft Computing) FL GA NN

  4. Overview Interactive Evolutionary Computation (IEC) • Difficulty of optimization problems in interactive system • Outputs must be subjectively evaluated (graphics or music) • Definition • Evolutionary computation that optimizes systems based on subjective human evaluation • Fitness function is replaced by a human user My goal is … Target system f(p1,p2,…,pn) System output Subjective evaluation Interactive EC From Takagi (2001)

  5. Overview Inherent Problems in IEC • Human fatigue problem • Common to all human-machine interaction systems • IEC • Acceleration of EC convergence with small population size and a few generation numbers • Usually within 10 or 20 search generations • Limitation of the individuals simultaneously displayed on a screen • Limitation of the human capacity to memorize the time-sequentially displayed individuals • Requirement to minimize human fatigue I’m tired

  6. Overview Direct Manipulation Media Retrieval & Design Media Retrieval & Design Partial Evaluation IEC IEC Domain Knowledge Proposed Conventional Proposed Framework

  7. Methodology Interactive Genetic Algorithm

  8. Methodology Knowledge-based Encoding New Search Space Removing impractical solutions Effective encoding? Using partially known information about the final solution Domain Knowledge Focusing on a specific search space Encoding in a modular manner Search Space

  9. Methodology Partial Evaluation based on Clustering Issues  Clustering algorithm selection, Indirect fitness allocation strategy

  10. Methodology It is very similar to the final solution but a bit different. Direct Manipulation of Evolution IGA Direct Manipulation Proof of usefulness of the direct manipulation  NK-landscape

  11. Applications Overview Humanized Media Retrieval & Design Media Retrieval Media Design Image Retrieval Video Retrieval Music Retrieval Fashion Design Flower Design Intrusion Pattern Design

  12. Applications: Image Retrieval Content-based Image Retrieval • Keyword-based method • Much time and labor to construct indexes in a large databases • Performance decrease when index constructor and user have different point of view • Inherent difficulty to describe image as a keyword • Content-based method • Image contents as a set of features extracted from image • Specifying queries using the features

  13. Applications: Image Retrieval Motivation • Systems for content-based image retrieval • QBIC (IBM, 1993) • QVE (Hirata and Kato, 1993) • Chabot (Berkeley, 1994) • Needs for image retrieval based on human intuition • Difficult to get perfect expression by queries • Lack of expression capability

  14. Applications: Image Retrieval System Structure

  15. Applications: Image Retrieval Original image Transformed image Chromosome Chromosome Structure R G B 0 1 2 3 49 50

  16. Applications: Image Retrieval Initial population The 8th population Convergence Test • The case of gloomy image retrieval

  17. Applications: Image Retrieval 95% Confidence interval 99% Subjective Test by Sheffe’s Method

  18. Applications: Video Retrieval Motivation • Evolution of computing technology (Huge computing storage, networking) • Necessary for management of video data transfer, processing and retrieval • Change of view point • Query by keyword  Query by content  Difficult to represent human’s semantic information • Query by semantics • Emotion-based retrieval

  19. Applications: Video Retrieval Hierarchical Structure of Video

  20. Applications: Video Retrieval System Architecture

  21. Applications: Video Retrieval Chromosome Structure

  22. Applications: Video Retrieval System Interface

  23. Applications: Video Retrieval User’s Satisfaction

  24. Applications: Music Retrieval Emotional Music Retrieval • Music information retrieval  Immature field • Query by singing • Query by content • Uploading pre-recording humming via web browser • Typing the string to represent the melody contour • Preprocessing • Query by humming  transforms user’s humming into symbolized representation • Conventional music retrieval system • Fast and effective extraction of musical patterns and transformation of user’s humming into systematic notes • Problem : User cannot remember some parts of melody

  25. Applications: Music Retrieval Power Spectrum Analysis a : classic b : jazz c : rock d : news channel

  26. Applications: Music Retrieval System Architecture

  27. Applications: Music Retrieval Chromosome Structure

  28. Applications: Music Retrieval Convergence Test (1)

  29. Applications: Music Retrieval Convergence Test (2)

  30. Applications : Fashion Design Change of Consumer Economy Before the Industrial Revolution : Customers have few choices on buying their clothes Manufacturer Oriented After the Industrial Revolution : Customers can make their choices with very large variety Near Future : Customers can order and get clothes of their favorite design Consumer Oriented

  31. Applications : Fashion Design Need for Interactive System • Almost all consumers are non-professional at design • To make designers contact all consumers is not effective • Need for the design system that can be used by non-professionals

  32. Applications : Fashion Design Fashion Design • Definition • To make a choice within various styles that clothes can take • Three shape parts of fashion design • Silhouette • Detail • Trimming

  33. Applications : Fashion Design System Architecture

  34. Applications : Fashion Design Knowledge-based Encoding A B C D E F Total 23 bits E : Skirt and waistline style(9) F : Color(8) B : Color(8) A : Neck and body style(34) … … D : Color(8) C : Arm and sleeve style(11) Search space size =34*8*11*8*9*8 =1,880,064 …

  35. Applications : Fashion Design Direct Manipulation Interface

  36. Applications : Fashion Design Fitness Changes for Encoding Schemes

  37. Applications : Fashion Design Hypercube Analysis Shortest Path Using DM Method One-Mutant Neighbors using Genetic Operator

  38. Applications : Flower Design Motivation • Proposed Method • Representation of knowledge-based genotype using the structure of real flower • Automatic generation of creatures • Process • Directed graph: Define the genotype of flower structure • IGA: Evaluation of created individuals and automatic creation • Math Engine: Representation of phenotype • Objective • Creation of character morphology similar to real flower • Find optimal solution in small solution space using knowledge-based genotype representation such as directed graph • Automatic creation of character using IGA

  39. Applications : Flower Design Related Works • Tree, flower and feather modeling • L-System • Box-based artificial characters (Karl Sims) evolution • Graph model representation • Golem • Robotic form and controller evolution • Graph-based data structure

  40. Applications : Flower Design Overall Procedure

  41. Applications : Flower Design Chromosome Structure

  42. Applications : Flower Design Convergence Test

  43. Applications : Flower Design Subjective Test by Sheffe’s Method

  44. Applications : Intrusion Pattern Design Motivation (1) • Various intrusion detection systems • For evaluating, IDS uses known intrusions • Possibility of having different patterns within identical intrusion type • Difficult to detect transformed intrusions • Problem • For better evaluation, intrusion patterns are needed more • Difficult to generate all possible proper intrusion patterns manually • Automatic generation is needed

  45. Applications : Intrusion Pattern Design Motivation (2) • Evolutionary pattern generation • Needs a variety of intrusion patterns • It is possible to generate new patterns by combining primitives • Generation of transformed intrusion patterns using simple genetic algorithm • IGA-based intrusion pattern generation • Difficult to estimate the fitness of patterns automatically • Using interactive genetic algorithm for the evaluation of patterns

  46. Applications : Intrusion Pattern Design Method • Intrusive behavior can be conducted in 5 steps • Proposed by DARPA • Possible path of U2R attacks

  47. Applications : Intrusion Pattern Design Known Intrusion Patterns IGA Intrusion Patterns GA 000100..... Initial Population Fitness Evaluation 001100..... Exploit Code Generate Audit Data System Architecture

  48. Concluding Remarks • Humanized CI framework using IEC • Knowledge-based encoding • Partial evaluation based on clustering • Direct manipulation of evolution with NK-landscape • Applications in media retrieval & design • Image, video and music retrievals • Fashion, flower and intrusion pattern designs • Contribution of humanized CI to engineering fields  Developing Emotional HC Interface by Evolving models through Interaction with Humans

More Related