Neural Optimization of Neural Optimization of Evolutionary Algorithm Evolutionary Algorithm Strategy ParametersStrategy Parameters

Hiral PatelHiral Patel

OutlineOutline

Why optimize parameters of an EA?Why optimize parameters of an EA? Why use neural networks?Why use neural networks? What has been done so far in this What has been done so far in this

field?field? Experimental ModelExperimental Model Preliminary Results and ConclusionPreliminary Results and Conclusion QuestionsQuestions

Why optimize parameters Why optimize parameters of an EA?of an EA?

Faster convergenceFaster convergence Better overall resultsBetter overall results Avoid premature convergenceAvoid premature convergence

Why use neural networks?Why use neural networks?

Ability to learnAbility to learn AdaptabilityAdaptability Pattern recognitionPattern recognition Faster then using another EAFaster then using another EA

What has been done so far What has been done so far in this field?in this field?

Machine Learning primarily Machine Learning primarily used to optimize ES and EPused to optimize ES and EP

Optimized mutation operatorsOptimized mutation operators Little has been done to Little has been done to

optimize GA parametersoptimize GA parameters

Experimental Model Experimental Model OutlineOutline

Neural Network BasicsNeural Network Basics Hebbian LearningHebbian Learning Parameters of the Genetic Parameters of the Genetic

Algorithm to be optimizedAlgorithm to be optimized Neural Network InputsNeural Network Inputs

Neural Network BasicsNeural Network Basics

Weight update algorithm

wq1(k)

wq2(k)

wqn(k)

vq(k)

dq(k)

( )e k

g(•)=f’(•)

yq(k)

bq(k) bias

Vector inputsignal

x(k)Rn1

x(k)Rn1

Deviation of activation function

Synaptic weights

Desired neuron

response

Neuron response(output)

Sigmoid activation function

f(•)

Adapted from: Adapted from: Ham, M. H., Kostanic, I  Principles of Neurocomputing for Science and Engineering, McGraw-Hilll, NY, 2001Ham, M. H., Kostanic, I  Principles of Neurocomputing for Science and Engineering, McGraw-Hilll, NY, 2001

Hebbian LearningHebbian Learning

Unsupervised learningUnsupervised learning Time-dependentTime-dependent Learning signal and Forgetting Learning signal and Forgetting

factorfactor

Hebb Learning for Hebb Learning for single neuronsingle neuron

Standard Hebbianlearning rule

{,}

x1

xn

w0

w1

wn

vf(v) y

( )( )

d vl y f v

dv

Adapted from: Adapted from: Ham, M. H., Kostanic, I  Principles of Neurocomputing for Science and Engineering, McGraw-Hilll, NY, 2001Ham, M. H., Kostanic, I  Principles of Neurocomputing for Science and Engineering, McGraw-Hilll, NY, 2001

x0

Parameters of the Genetic Parameters of the Genetic Algorithm to be optimizedAlgorithm to be optimized

Crossover ProbabilityCrossover Probability Crossover Cell DividerCrossover Cell Divider Cell Crossover ProbabilityCell Crossover Probability Mutation ProbabilityMutation Probability Mutation Cell DividerMutation Cell Divider Cell Mutation ProbabilityCell Mutation Probability Bit Mutation ProbabilityBit Mutation Probability

Neural Network InputsNeural Network Inputs

Current Parameter ValuesCurrent Parameter Values VarianceVariance MeanMean Max fitnessMax fitness Average bit changes for crossoverAverage bit changes for crossover Constant parameters of the GAConstant parameters of the GA

Preliminary ResultsPreliminary Results

Tests run with Knapsack problem Tests run with Knapsack problem with dataset 3, pop. size 800, rep. with dataset 3, pop. size 800, rep. size 1600size 1600

Learning Signal and Forgetting Learning Signal and Forgetting factor are not yet optimal enough factor are not yet optimal enough to suggest better performance to suggest better performance with NNwith NN

Output for 1600 Output for 1600 generationsgenerations

-100

0

100

200

300

400

500

0 500 1000 1500 2000

Fitness

Mean

Variance

CCD

MCD

Probabilities for 1600 Probabilities for 1600 generationsgenerations

0

0.2

0.4

0.6

0.8

1

1.2

0 500 1000 1500 2000

CP

CCP

MP

CMP

BMP

ConclusionConclusion

It may be possible to get better It may be possible to get better performance out of a Neural performance out of a Neural Optimized EA as long as the Optimized EA as long as the (unsupervised) Neural Network is (unsupervised) Neural Network is able to adapt to the changes able to adapt to the changes quickly and to recognize local quickly and to recognize local minima.minima.

Possible Future WorkPossible Future Work ES to optimize parameters, use a ES to optimize parameters, use a

SOM to do feature extraction of the SOM to do feature extraction of the optimized parameter values, use the optimized parameter values, use the SOM output as codebook vectors for SOM output as codebook vectors for LVQ network and then classify the LVQ network and then classify the output of the original ES, use the output of the original ES, use the classifications to perform supervised classifications to perform supervised training of Levenberg-Marquardt training of Levenberg-Marquardt Backpropagation network to form Backpropagation network to form rule set.rule set.

Question ?Question ?