Interactive evolutionary computation a framework and applications
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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

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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

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


Interactive evolutionary computation a framework and applications

Overview

Interactive Computational Intelligence

Interaction/Evolution

Human

Computer

Interface

User Modeling

(Efficiency)

(Emotion)

AI

(Soft Computing)

FL

GA

NN


Interactive evolutionary computation iec

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)


Inherent problems in iec

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


Proposed framework

Overview

Direct

Manipulation

Media Retrieval

& Design

Media Retrieval

& Design

Partial

Evaluation

IEC

IEC

Domain Knowledge

Proposed

Conventional

Proposed Framework


Interactive genetic algorithm

Methodology

Interactive Genetic Algorithm


Knowledge based encoding

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


Partial evaluation based on clustering

Methodology

Partial Evaluation based on Clustering

Issues  Clustering algorithm selection, Indirect fitness allocation strategy


Direct manipulation of evolution

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


Overview

Applications

Overview

Humanized Media Retrieval & Design

Media Retrieval

Media Design

Image Retrieval

Video Retrieval

Music Retrieval

Fashion Design

Flower Design

Intrusion Pattern

Design


Content based image retrieval

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


Motivation

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


System structure

Applications: Image Retrieval

System Structure


Chromosome structure

Applications: Image Retrieval

Original image

Transformed image

Chromosome

Chromosome Structure

R

G

B

0 1 2 3

49 50


Convergence test

Applications: Image Retrieval

Initial population

The 8th population

Convergence Test

  • The case of gloomy image retrieval


Subjective test by sheffe s method

Applications: Image Retrieval

95%

Confidence interval

99%

Subjective Test by Sheffe’s Method


Motivation1

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


Hierarchical structure of video

Applications: Video Retrieval

Hierarchical Structure of Video


System architecture

Applications: Video Retrieval

System Architecture


Chromosome structure1

Applications: Video Retrieval

Chromosome Structure


System interface

Applications: Video Retrieval

System Interface


User s satisfaction

Applications: Video Retrieval

User’s Satisfaction


Emotional music retrieval

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


Power spectrum analysis

Applications: Music Retrieval

Power Spectrum Analysis

a : classic

b : jazz

c : rock

d : news channel


System architecture1

Applications: Music Retrieval

System Architecture


Chromosome structure2

Applications: Music Retrieval

Chromosome Structure


Convergence test 1

Applications: Music Retrieval

Convergence Test (1)


Convergence test 2

Applications: Music Retrieval

Convergence Test (2)


Change of consumer economy

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


Need for interactive system

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


Fashion design

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


System architecture2

Applications : Fashion Design

System Architecture


Knowledge based encoding1

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


Direct manipulation interface

Applications : Fashion Design

Direct Manipulation Interface


Fitness changes for encoding schemes

Applications : Fashion Design

Fitness Changes for Encoding Schemes


Hypercube analysis

Applications : Fashion Design

Hypercube Analysis

Shortest Path Using DM Method

One-Mutant Neighbors using Genetic Operator


Motivation2

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


Related works

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


Overall procedure

Applications : Flower Design

Overall Procedure


Chromosome structure3

Applications : Flower Design

Chromosome Structure


Convergence test1

Applications : Flower Design

Convergence Test


Subjective test by sheffe s method1

Applications : Flower Design

Subjective Test by Sheffe’s Method


Motivation 1

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


Motivation 2

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


Method

Applications : Intrusion Pattern Design

Method

  • Intrusive behavior can be conducted in 5 steps

    • Proposed by DARPA

    • Possible path of U2R attacks


System architecture3

Applications : Intrusion Pattern Design

Known

Intrusion Patterns

IGA

Intrusion

Patterns

GA

000100.....

Initial Population

Fitness

Evaluation

001100.....

Exploit

Code

Generate

Audit Data

System Architecture


Concluding remarks

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


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