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In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.
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Modeling Selection Pressure in XCS for Proportionate and
Tournament SelectionAlbert Orriols-Puig1,2 Kumara Sastry2
Pier Luca Lanzi1,3 David E. Goldberg2
Ester Bernadó-Mansilla1
1Research Group in Intelligent SystemsEnginyeria i Arquitectura La Salle, Ramon Llull University
2Illinois Genetic Algorithms LaboratoryDepartment of Industrial and Enterprise Systems Engineering
University of Illinois at Urbana Champaign
3Dipartamento di Elettronica e InformazionePolitecnico di Milano
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 2GECCO’07
Motivation
Facetwise modeling to permit a successful understanding of complex systems (Goldberg, 2002)
Analysis of selection schemes in XCS: Proportionate vs. Tournament
– Tournament selection is more robust to parameter settings and noise than proportionate selection (Butz, Sastry & Goldberg, 03, 05)
– Proportionate selection is, at least, as robust as tournament selection if the appropriate fitness separation is used (Karbat, Bull & Odeh, 05)
In GA, these schemes were studied through the analysis of takeover time(Goldberg & Deb, 90; Goldberg, 02)
Aim: Model selection pressure in XCS through the analysis of the takeover time
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 3GECCO’07
Outline
1. Description of XCS
2. Modeling takeover time
3. Comparing the two models
4. Experimental validation
5. Modeling generality
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 4GECCO’07
Description of XCS
Representation:– fixed-size rule-based representation
– Rule Parameters: Pk, εk, Fk, nk
Learning interaction:– At each learning iteration Sample a new example
– Create the match set [M]
– Each classifier in [M] votes in the prediction array
– Select randomly an action and create the action set [A]
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 5GECCO’07
Description of XCS
Rule evaluation: reinforcement learning techniques.– Prediction:
– Prediction error:
– Accuracy of the prediction:
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 6GECCO’07
Description of XCS
Rule evaluation: reinforcement learning techniques.– Relative accuracy:
– Fitness computed as a windowed average of the accuracy:
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 7GECCO’07
Description of XCS
Rule discovering:– Steady-state, niched GA
– Population-wide deletion
Proportionate selection• Probability proportionate to rule’s fitness.
Tournament selection• Selects τ percent of classifiers from [A]
• Selects the classifier with higher microclassifier fitness
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 8GECCO’07
Outline
1. Description of XCS
2. Modeling takeover time
3. Comparing the two models
4. Experimental validation
5. Modeling generality
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 9GECCO’07
Modeling Takeover Time
In GA:– Usually, the fitness of an individual is constant
– Selection and replacement are performed over the whole population
In XCS:– Fitness depends on the other rule’s fitness in the same niche
– Selection is niched-based, whilst deletion is population-wide
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 10GECCO’07
Modeling Takeover Time
Assumptions in our model– XCS has evolved a set of non-overlapping niches
– Simplified scenario: niche with two classifiers cl1 and cl2.
– cl1 is the best rule in the niche: k1 > k2
– Classifier clk has: • prediction error εk
• fitness Fk
• numerosity nk
• microclassifier fitness fk
– cl1 and cl2 are equally general Same reproduction opportunities
– We assume niched deletion
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 11GECCO’07
Proportionate Selection
Fitness is an average of cl1 and cl2 respective accuracies
Then, the probability of selecting the best classifier cl1 is:
Probability of deletion: Pdel(clj) = nj/n where n=n1+n2
Num. ratio: nr = n2/n1Accuracy ratio: ρ=k2/k1
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 12GECCO’07
Proportionate Selection
Evolution of cl1 numerosity
1. The numerosity of cl1 increases if cl1 is selected by the GA and another classifier is selected to be deleted
2. The numerosity of cl1 decreases if cl1 is not selected by the GA but it is selected by the deletion operator
3. The numerosity of c1 remain de same otherwise.
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 13GECCO’07
Proportionate Selection
Grouping the above equations we obtain
Rewritten in terms of proportion of classifiers cl1 in the niche:
Considering Pt+1 – Pt ≈ dp/dt
Pt = n1/n
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Integrate:Initial proportion: P0Final proportion: P
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 14GECCO’07
Proportionate Selection
This gives us that the takeover time for proportionateselection is guided by the following expression:
P0: initial proportion of cl1 in the niche
P: final proportion of cl1 in the niche
ρ: accuracy ratio between cl2 and cl1n: niche size
If ρ 1: trws ≈ ∞
If ρ 0:
A higher separation between fitness enables a higher ability in identifying accuraterules, as announced by Karbat, Bull & Odeh, 2005.
n
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 15GECCO’07
Tournament Selection
Assumptions– Fixed tournament size s
– cl1 is the best classifier in the niche: f1 > f2 , that is, F1/n1 > F2 /n2
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 16GECCO’07
Tournament Selection
This gives us that the takeover time for tournamentselection is guided by the following expression:
P0: initial proportion of cl1 in the niche
P: final proportion of cl1 in the niche
s: tournament size
n: niche size
Tournament selection does not depend on the accuracy ratio between the best classifier and the others in the same [A], as pointed by Butz, Sastry & Goldberg, 2005
As s increases, this expression decreases
It does not depend on the individual fitness
For large s:
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 17GECCO’07
Outline
1. Description of XCS
2. Modeling takeover time
3. Comparing the two models
4. Experimental validation
5. Modeling generality
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 18GECCO’07
Proportionate vs. Tournament
Values of s and ρ for which both schemes result in the same takeover time. Require: t*RWS = t*TS
– We obtain
For P0 = 0.01and P = 0.99
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 19GECCO’07
Outline
1. Description of XCS
2. Modeling takeover time
3. Comparing the two models
4. Experimental validation
5. Modeling generality
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 20GECCO’07
Design of Test Problems
Single-niche problem– One niche with 2 classifiers:
• Highly accurate classifier cl1:
• Less accurate classifier cl2:
• Varying ρ we are changing the fitness separation between cl1 and cl2
– Population initialized with• N · P0 copies of cl1
• N · (1 – P0) copies of cl2
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 21GECCO’07
Results on the Single-Niche Problem
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Accuracy ratio: ρ= 0.01
RWS
Tournament s=9
Tournament s=3
Tournament s=2
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 22GECCO’07
Results on the Single-Niche Problem
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Accuracy ratio: ρ= 0.50
RWS
Tournament s=9
Tournament s=3
Tournament s=2
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 23GECCO’07
Results on the Single-Niche Problem
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Accuracy ratio: ρ= 0.90
RWS
Tournament s=9
Tournament s=3
Tournament s=2
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 24GECCO’07
Design of Test Problems
Multiple-niche problem– Several niches with 1 maximally accurate classifier each niche.
– One over-general classifier that participates in all niches
– The population contains:• N · P0 copies of maximally accurate classifiers
• N · (1 – P0) copies of the overgeneral classifier
– The problem violates two assumptions of the model• Overlapping niches
• The size of the different niches differ from the population size
– Deletion can select any classifier in [P]
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 25GECCO’07
Results on the Multiple-Niche Problem
For small ρ the theory slightly underestimates the empirical takeover time
The model of proportionate selection is accurate in general scenarios if the ratio of accuracies is small
In situations where there is a small proportion of the best classifier in one niche competing with other slightly inaccurate and overgeneral, the overgeneral may take over the population.
Further experiments, show that for ρ >= 0.5, the best classifiers is removed from the population
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
RWS ρ = 0.01RWS ρ = 0.20
RWS ρ = 0.30RWS ρ = 0.40
RWS ρ = 0.50
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 26GECCO’07
Results on the Multiple-Niche Problem
For high s the theory slightly underestimates the empirical takeover time
The model of tournament selection is accurate in general scenarios if the tournament size is high enough
Only in the extreme case (s=2), the experiments strongly disagree with the theory
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
TS s = 2TS s = 3
TS s = 9
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 27GECCO’07
Outline
1. Description of XCS
2. Modeling takeover time
3. Comparing the two models
4. Experimental validation
5. Modeling generality
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 28GECCO’07
Modeling Generality
Scenario– The best classifier cl1 appears in the niche with probability 1
– cl2 appears in the niche with probability ρm
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 29GECCO’07
Proportionate Selection
The takeover time for proportionate selection is guided by the following expression:
P0: initial proportion of cl1 in the niche
P: final proportion of cl1 in the niche
ρ: accuracy ratio between cl2 and cl1ρm: occurrence probability of cl2N: niche size
If cl1 is either more accurate or more general than cl2, cl1 will take over the population.
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 30GECCO’07
Tournament Selection
The takeover time for tournament selection is guided by the following expression:
P0: initial proportion of cl1 in the niche
P: final proportion of cl1 in the niche
s: tournament size
ρm: occurrence probability of cl2
n: niche size
For low ρm or high s the right-hand logarithm goes to zero, so that the takeover timemainly depends on P0 and P
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 31GECCO’07
Results of the Extended Model onthe one-niched Problem
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
RW
S
Tour
nam
ent
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 32GECCO’07
Outline
1. Description of XCS
2. Modeling takeover time
3. Comparing the two models
4. Experimental validation
5. Modeling generality
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 33GECCO’07
Conclusions
We derived theoretical models for proportionate and tournament under some assumptions– Models are exact in very simple scenarios
– Models can qualitatively explain both selection schemes in more complicated scenarios
Models support that tournament is more robust (Butz, Sastry & Goldberg, 2005)
Fitness separation is essential to guarantee that the best classifier will take over the population in proportionate selection (Karbhat, Bull & Oates, 2005)– It may fail in domains where there are slightly inaccurate classifiers
(real-world domains).
– Eg: Real-world problems where many slightly inaccurate and more general classifiers may take over accurate classifiers.
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Modeling Selection Pressure in XCS for Proportionate and
Tournament Selection
Albert Orriols-Puig1,2 Kumara Sastry2
Pier Luca Lanzi2 David E. Goldberg2
Ester Bernadó-Mansilla1
1Research Group in Intelligent SystemsEnginyeria i Arquitectura La Salle, Ramon Llull University
2Illinois Genetic Algorithms LaboratoryDepartment of Industrial and Enterprise Systems Engineering
University of Illinois at Urbana Champaign
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 35GECCO’07
Tournament Selection
Evolution of cl1 numerosity
1. The numerosity of cl1 increases if cl1 participates in the tournament and another classifier is selected to be deleted
2. The numerosity of cl1 decreases if cl1 does not participate in the tournament but it is selected by the deletion operator
3. The numerosity of c1 remain de same otherwise.
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Probability that the cl1 participates in the tournament
Probability that the cl1 participates in the tournament
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 36GECCO’07
Results on the Single-Niche Problem
Empirical results perfectly match theory
For ρ=0.01 proportionate selection produces a faster increase
Roulette wheel selection is negatively influenced by the increase of ρ
Coherently to what was empirically shown in (Butz& Sastry, 2003), tournament selection is more robust to variations on the fitness scaling.
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 37GECCO’07
Description of XCS
Representation:– fixed-size rule-based representation
– Michigan-style LCS: solution represented by the entire rule-set
– Map of condition-action-predictions.
Rule evaluation: reinforcement learning techniques.– Prediction:
– Prediction error:
– Accuracy of the prediction:
)pβ(Rpp kkk −+=
)- |pRβ(| kkkk εεε −+=
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 38GECCO’07
Proportionate vs. Tournament
Compare the values of values of s and ρ
for which both schemes have the same initial increase of the proportion of the best classifier
for which both schemes have the same takeover time
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 39GECCO’07
Tournament Selection
Grouping the above equations we obtain
Rewritten in terms of proportion of classifiers cl1 in the niche:
Considering Pt+1 – Pt ≈ dp/dt
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 40GECCO’07
Proportionate vs. Tournament
Values of s and ρ for which both schemes have the same initial increase of the proportion of the best classifier
– Requiring that
– We obtain
The tournament size s has to increase as ρdecreases
Tournament selection produces a stronger pressure toward the best classifier in scenarios in which slightly inaccurate but initially hihgly numerous classifiers are competing against highly accurate classifiers
For P0 = 0.01and P = 0.99
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 41GECCO’07
Modeling Generality
Scenario– The best classifier cl1 appears in the niche with probability 1
– cl2 appears in the niche with probability ρm
Model for proportionate selection
1. The numerosity of cl1 increases if cl1 and cl2 appear in the niche, cl1 is selected by the GA, and cl2 is chosen by deletion, or when cl1 is the only classifier in the niche and another classifier is deleted.
2. The numerosity of cl1 decreases if cl1 and cl2 are in the niche, cl1 is not selected by the GA but is selected by deletion
3. The numerosity of c1 remain de same otherwise.
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 42GECCO’07
Proportionate Selection
Which gives us that the takeover time for proportionateselection is guided by the following expression:
P0: initial proportion of cl1 in the niche
P: final proportion of cl1 in the niche
ρ: accuracy ratio between cl2 and cl1ρm: occurrence probability of cl2N: niche size
If cl1 is either more accurate or more general than cl2, cl1 will take over the population.
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 43GECCO’07
Tournament Selection
Evolution of cl1 numerosity
1. The numerosity of cl1 increases if cl1 and cl2 appear in the niche, cl1 is selected by the GA, and cl2 is chosen by deletion, or when cl1 is the only classifier in the niche and another classifier is deleted.
2. The numerosity of cl1 decreases if cl1 and cl2 are in the niche, cl1 is not selected by the GA but is selected by deletion
3. The numerosity of cl1 remain de same otherwise.
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 44GECCO’07
Tournament Selection
Which gives us that the takeover time for tournamentselection is guided by the following expression:
P0: initial proportion of cl1 in the niche
P: final proportion of cl1 in the niche
s: tournament size
ρm: occurrence probability of cl2
n: niche size
For low ρm or high s the right-hand logarithm goes to zero, so that the takeover timemainly depends on P0 and P
1. Description of XCS2. Modeling Takeover Time3. Comparing the two Models4. Experimental Validation5. Modeling Generality6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 45GECCO’07
Aim
Model selection pressure in XCS through the analysis of the takeover time– Consider that XCS has converged to an optimal solution
– Write differential equations that describe the change in proportion of the best individual
– Solve the equations and derive a closed form solution
– Validate the model empirically