I’
sec"
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Combating User Fatigue in iGAs: 
Partial Ordering,  Support Vector
Machines,  and Synthetic Fitness

Xavier Ll...
Human and Social Aspects of GAS

c—: -:

5

Standard Interactive
Genetic Algorithms Genetic Algorithms )

‘J

Computer  Hu...
- Interactive Genetic Algorithms

Motivation

No quantitative fitness is available

Qualitative fitness is provided by the...
Interactive Genetic Algorithms

- Human in charge of the evaluation of a solution

- A main challenge:  user fatigue
- Sub...
Facets of an Interactive GAS

8 Users need a clear idea of the outcome

- Need for a clear criteria of the goal to reach

...
Lack of a Real Fitness

- A minimal iGA scenario

- Two solutions are presented for user evaluation

- Three possible eval...
Properties of a Synthetic Fitness

- Solution-quality order should be maintained

- Any synthetic fitness needs to maintai...
Order Maintenance

0 y =  a*x + b
- a>1 guaranties the order

- Tournament only cares
about the partial order
among soluti...
fix)

 Fitness Extrapolation (1/11)

100

. ..m. .,. . K 3 ° Set of evaluated solutions

Esurnatod Iltnuss

3 j - New solut...
fix)

GECCO 2005

Fitness Extrapolation (II/ II)

/  4

Regression model

Real fitness
Estimated limos: 

Extrapolates beyo...
V.  Accuracy of Fitness Extrapolation
 Using e—SVlVI

Problem slm =  4,7,‘l0,13

P(eflOl<0. 5)
Ntlnber of instances (995)

...
Yes,  but. ..

- If we have a numerical fitness: 

- We can build a regression model
- We can use such model for combating...
GE 1: Ci

Hierarchical Evaluation Order

I * . ' I ‘ l *

o1o111_o1o1oo', o1o1o1, 1oooo1’ 1ooooo_ 1o1o1o' , oo1ooo_foo111o...
Partial Order Graph

 

)fo1o1ooj.  ,fo1o1oo: l'
,   )_ 3 ,  (2    ,4 S.  :
, o1o111,1-: _ ~, o1o1o1; >,1oooo1,1 _010111't...
The Numeric Fitness

 

- Dominance meassure

Dominates Dominated by Difference

  

     

  

   

,2 2
; _0101o0,i ,  ....
GE 1: Ci

1.
2.
3.

Synthetic Fitness

Collect the user evaluations
Build the partial order graph

Compute the numeric fit...
- Cut down the number of human evaluations

- Exploit synthetic fitness
- Optimize it
- Sample the best candidates
- Show ...
GECIJ‘

The Big Picture

 

Evaluation
_ .11.
V 
Sampling Samp/ /ngx

4’. ..

010111’

Optimizer ‘=  —. I§(x)‘1
Synthetic ...
A Real Experiment

- Considerations in the design process

- Clear goal definition
- Impact of problem visualization
- Per...
G1

I‘! 

(w

Fv

ca
cw

DISCUS & iGAs

 

' DISCUS

Current session status:  Iteration l C'cm, :~m'. -‘.92: 16.

V ' ' Ca...
I‘

- The system

Setting The Pieces

A simple web application
OneMax task

No linkage learning needed
2-SVM and a linear ...
as 2 r—— re ————r ~~ ewe 20—— —r 2 — —r ~~
0 simolu GA
II!  -" Covwiirptnce-lime Nodal 0
3" __ I6
= ‘ 3 § 14
E E 12
§  3 I...
Conclusions

- Address the lack of numerical fitness and fatigue
- Synthetic fitness model of user preferences

- Optimize...
sect ’
- “l

GECCO 2005

DISCUS project web page
http:  I / www. i-discus. org

llliGAL web site
http: //www-illigal. ge. ...
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Combating user fatigue in iGA: partial ordering, support vector machines, and synthetic fitness

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Combating user fatigue in iGA: partial ordering, support vector machines, and synthetic fitness

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Combating user fatigue in iGA: partial ordering, support vector machines, and synthetic fitness

  1. 1. I’ sec" (- Combating User Fatigue in iGAs: Partial Ordering, Support Vector Machines, and Synthetic Fitness Xavier Llora, Kumara Sastry, David E. Goldberg, Abhimanyu Gupta, and Lalitha Lakshmi Illinois Genetic Algorithms Lab University of Illinois at Urbana-Champaign ixllora, kumara, deg, agupta21}@i11igal . ge . uiuc . edu
  2. 2. Human and Social Aspects of GAS c—: -: 5 Standard Interactive Genetic Algorithms Genetic Algorithms ) ‘J Computer Human-Based 995%" (CAD) Genetic Algorithms compu»a~o~a/ Selection agent Recombination agent (Kosorukoff & Goldberg, 2002) GECCO 2005 Llori. Sastly. Goldserg, Gupta, E Lakshmi 2
  3. 3. - Interactive Genetic Algorithms Motivation No quantitative fitness is available Qualitative fitness is provided by the user o Sorting of several solutions - Choosing one of solution out of a subset Big time scale between user evaluations and the evolutionary mechanisms User fatigue (short time periods 1-2 hours) Frustration (repeated evaluation of similar solutions) - Efficiency enhancement for iGAs GECCO 2005 Llora, Sastry, Goldberg, Gupta, & Lakshmi 3
  4. 4. Interactive Genetic Algorithms - Human in charge of the evaluation of a solution - A main challenge: user fatigue - Suboptimal solutions - Frustration - How to combat user fatigue? - Cut down the number of evaluations - Avoid repetitive evaluations - Look for eureka moments - Evaluation-relaxation schemes (Sastry, 2002) and fitness estimation (Sastry, Goldberg, & Pelikan, 2004) - Require a numerical fitness that can be computed GECCO 2005 Llora, Sastry, Goldberg, Gupta, 8 Lakshmi 4
  5. 5. Facets of an Interactive GAS 8 Users need a clear idea of the outcome - Need for a clear criteria of the goal to reach 8 A good visualization goes a long way - A non-intuitive visualization may mislead user's evaluations V Lack of numerical fitness can be a problem - No numeric form that can be optimize is available 0/ User fatigue needs to be minimized - User may only be able to provide reliable evaluations for short time periods (1-2 hours) at Users tend to change their criteria along the way - Easy to maintain an unique criteria for short time periods GECCO 2005 Llora, Sastry, Goldberg, Gupta, & Lakshmi 5
  6. 6. Lack of a Real Fitness - A minimal iGA scenario - Two solutions are presented for user evaluation - Three possible evaluation outcomes: 1. The first is better than the second 2. The second is better than the first 3. Don’t know, don't care - Evaluation by comparison - The GA equivalent - Tournament selection s=2 - Can we compute a numerical fitness out of partial user evaluations? GECCO 2005 Llora, Sastry, Goldberg, Gupta, 8 Lakshmi 6
  7. 7. Properties of a Synthetic Fitness - Solution-quality order should be maintained - Any synthetic fitness needs to maintain the solution ordering provided by the user If $12 $2 2 2 5,, then fs(S1) 2 fs(s2) 2 2 fs(Sn) - Synthetic fitness should allow extrapolation - Any synthetic fitness needs to be able to generalize the ordering relation beyond the available evaluations collected GECCO 2005 Llora, Sastry, Goldberg, Gupta. & Lakshmi 7
  8. 8. Order Maintenance 0 y = a*x + b - a>1 guaranties the order - Tournament only cares about the partial order among solutions - Low cost, high error model GECCO 2005 U. or'a, Sastry, Goldberg, Gupta, 8 Lakshmi B
  9. 9. fix) Fitness Extrapolation (1/11) 100 . ..m. .,. . K 3 ° Set of evaluated solutions Esurnatod Iltnuss 3 j - New solution - Compute the k-nearest neighbor - Assign the fitness of the K , weighted fitness of the k- ‘ nearest neighbors o I - No extrapolation beyond ° ‘° °‘’ °‘’ ‘°° the current limits Nearest Neighbor GECCO 2005 lJ. or'a, Sastry, Goldberg, Gupta, 8 Lakshmi 9
  10. 10. fix) GECCO 2005 Fitness Extrapolation (II/ II) / 4 Regression model Real fitness Estimated limos: Extrapolates beyond the evaluated solutions - Provides a direction toward improvements How many solutions we need to have a reliable model? 40 60 60 100 Support Vector Regression U. or'a, Sastry, Goldberg, Gupta, 8 Lakshmi 10
  11. 11. V. Accuracy of Fitness Extrapolation Using e—SVlVI Problem slm = 4,7,‘l0,13 P(eflOl<0. 5) Ntlnber of instances (995) 0 5 10 15 20 25 Number oi Same ‘as GECCO 2005 Llora, Sastry, Goldberg, Gupta, & Lakshmi
  12. 12. Yes, but. .. - If we have a numerical fitness: - We can build a regression model - We can use such model for combating fitness fatigue - What is it available? - Partial ordering of solutions - Incremental refinement (new evaluations) - The idea: - Use dominance measures on the graph of partially-ordered solutions GECCO 2005 Llora, Sastry, Goldberg, Gupta, & Lakshmi 12
  13. 13. GE 1: Ci Hierarchical Evaluation Order I * . ' I ‘ l * o1o111_o1o1oo', o1o1o1, 1oooo1’ 1ooooo_ 1o1o1o' , oo1ooo_foo111o' Given a set of solutions - They can be presented as a sequence of hierarchical tournaments - Obtain the partial ordering of the solutions - Such ordering can be expressed in a graph form Llora, Sastry, Goldberg, Gupta, 8 Lakshmi
  14. 14. Partial Order Graph )fo1o1ooj. ,fo1o1oo: l' , )_ 3 , (2 ,4 S. : , o1o111,1-: _ ~, o1o1o1; >,1oooo1,1 _010111't’_“'-,010101,i7>l‘100OO1, _ _1 ,1 _, , ,1 101010; 4100000, ,1o1o1o; '~_9,_1ooooo, l . I' . T, ': _oo111oj%~: oo1ooo; I 3'", ‘ii ' . __001 1 10!‘ 001000) - Nodes are solutions - Edges represent the evaluation provided by the user 1. The first is better than the second 2. The second is better than the first 3. Don’t know, don’t care - Transformed to contain only 1 and 2 relations - Property: cycles detect user contradictions in evaluations GECCO 2005 U. or'a, Sastry, Goldberg, Gupta, 8 Lakshmi 14
  15. 15. The Numeric Fitness - Dominance meassure Dominates Dominated by Difference ,2 2 ; _0101o0,i , . 010111 4 5 . 1 2 4/ ,1 .4 ,1,’ 010100 2 0 -1 4 i_010111,(—><_ ,0101o1,l—>(1o00o1,i 010101 3 1 0 3 "-, ;V 100001 2 0 -2 5 G . a G‘: 100000 1 0 -3 0 (101010]-§_10o0oo, l 101010 3 2 2 2 }_ 001000 1 0 -3 0 Kg _, /1 0 , 001110 3 2 2 2 l00111o 0010ool _ Real Synthetic GECCO 2005 until, Sastry, Goldtcerg, Gupta, 8 Lakshmi 15
  16. 16. GE 1: Ci 1. 2. 3. Synthetic Fitness Collect the user evaluations Build the partial order graph Compute the numeric fitness using the partial order graph . Use 1-: -SVM for creating a regression model . Use the learned regression as the synthetic fitness Llora, Sastry, Goldberg, Gupta, 8 Lakshmi ‘(,1
  17. 17. - Cut down the number of human evaluations - Exploit synthetic fitness - Optimize it - Sample the best candidates - Show the best solutions to the user - The sample best solutions help combating - Fatigue (educated guess of user preference) - Frustration (produce new eureka solutions) GECCO 2005 Llora, Sastry, Goldberg, Gupta, 8 Lakshmi Fatigue I7
  18. 18. GECIJ‘ The Big Picture Evaluation _ .11. V Sampling Samp/ /ngx 4’. .. 010111’ Optimizer ‘= —. I§(x)‘1 Synthetic fitness ‘°’°‘°-" 001l10“‘ Llora, Sastry, Goldberg, Gupta, 8 Lakshmi 010111 010100 010101 100001 100000 10101!) 001000 001110 Partial order | update ‘v V , 010100,’ — 010101 — 100001 - 4100000‘ - 001000 '
  19. 19. A Real Experiment - Considerations in the design process - Clear goal definition - Impact of problem visualization - Persistence of user criteria - A simple controlled task - One Max GECCO 2005 lJor‘a, Sastry, Goldberg, Gupta, 8 Lakshmi 19
  20. 20. G1 I‘! (w Fv ca cw DISCUS & iGAs ' DISCUS Current session status: Iteration l C'cm, :~m'. -‘.92: 16. V ' ' Cam11'dale ' ' V V ‘ Cdlldiddlfl 39 Target —~—~ 1 38 target 7‘ ‘ 25 ‘ 25 ‘ 20 ' 20 ' 15 ‘ 15 , ‘ 10 - 10 ‘ 5 a’ 4 . / 5 1/ B 1 4 2 1 1 1. 3 C ' 1 1. 1 1 1 1 . _1 (1 5 1H 15 29 25 30 0 5 10 15 2H 25 30 Solution 1 Flame. select the best so-‘.0000 in r_) Lloré, Sastry, Goldberg, Gupta, 8 Lakshmi
  21. 21. I‘ - The system Setting The Pieces A simple web application OneMax task No linkage learning needed 2-SVM and a linear kernel The compact genetic algorithm (Harik, Cantu-Paz, Goldberg, & Miller, 1999) - Set up GECCO 2005 One user with no relation to the research Repeated series of 10 independent runs for different population sizes {4, 8, 12, 16, 20, 24, 28, 32} Collect the data to compare it to a simple GA Llora, Sastry, Goldberg, Gupta, 8 Lakshmi 21
  22. 22. as 2 r—— re ————r ~~ ewe 20—— —r 2 — —r ~~ 0 simolu GA II! -" Covwiirptnce-lime Nodal 0 3" __ I6 = ‘ 3 § 14 E E 12 § 3 ID 2 ‘6 E is E E 5 — 7 2 ‘cl 0 : N'K: Jv. ‘ GA 4 - -- Gainnim run model 0 GA a 5ST 2 5 — GA The 0 5 ID 15 20 25 30 5 10 is 20 25 30 1 Fvobkim 5.19, I Problem size. I mi Pi. |iul.1iinIi Sm llii (‘aim i. _- ll -' Tll1l~‘ 7 500i; 6 ma. 0A 0- , _ ll -- swcle--GAmeiry ‘ .5 : "T"‘ 0 iBAv'55F , - ; ; . ml; c;mx__ 10- 6, 3 " 1 § '0‘ ' z _ - . _ 1 10 is 20 an 5 10 is '27)-— ‘:7 —: -3- ' Vrbolcwi size, I Problem siz-3.! GECC0 2005 In ) hilirtinn I'1. ilii. iIi1-Ins id‘) Hlnnl up 22
  23. 23. Conclusions - Address the lack of numerical fitness and fatigue - Synthetic fitness model of user preferences - Optimize such model to take the advantage of the timescale difference - Sample new solutions out of the optimized model - Inject these solutions in the evaluation process - Remarkable speedups GECCO 2005 Llora, Sastry, Goldberg. Gupta, 8 Lakshmi Z3
  24. 24. sect ’ - “l GECCO 2005 DISCUS project web page http: I / www. i-discus. org llliGAL web site http: //www-illigal. ge. uiuc. edu llliGAL blog http: / / illigal. blogspot. com Llor'a. Sastry, Goldaerg, Gupta. 8 Lakshmi Thank you 24

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