Neural Optimization of Evolutionary Algorithm Strategy Parameters

1. Neural Optimization of Evolutionary Algorithm Strategy Parameters Hiral Patel

2. Outline • Why optimize parameters of an EA? • Why use neural networks? • What has been done so far in this field? • Experimental Model • Preliminary Results and Conclusion • Questions

3. Why optimize parameters of an EA? • Faster convergence • Better overall results • Avoid premature convergence

4. Why use neural networks? • Ability to learn • Adaptability • Pattern recognition • Faster then using another EA

5. What has been done so far in this field? • Machine Learning primarily used to optimize ES and EP • Optimized mutation operators • Little has been done to optimize GA parameters

6. Experimental Model Outline • Neural Network Basics • Hebbian Learning • Parameters of the Genetic Algorithm to be optimized • Neural Network Inputs

7. Neural Network Basics Sigmoid activation function bq(k)bias Adapted from: Ham, M. H., Kostanic, I  Principles of Neurocomputing for Science and Engineering, McGraw-Hilll, NY, 2001 wq1(k) Neuron response (output) f(•) Vector input signal wq2(k) vq(k) yq(k) x(k)Rn1 wqn(k) Synaptic weights dq(k) Desired neuron response Deviation of activation function g(•)=f’(•) Weight update algorithm x(k)Rn1

8. Hebbian Learning • Unsupervised learning • Time-dependent • Learning signal and Forgetting factor

9. Hebb Learning for single neuron x0 w0 Adapted from: Ham, M. H., Kostanic, I  Principles of Neurocomputing for Science and Engineering, McGraw-Hilll, NY, 2001 w1 v x1 f(v) y wn xn Standard Hebbian learning rule {,}

10. Parameters of the Genetic Algorithm to be optimized • Crossover Probability • Crossover Cell Divider • Cell Crossover Probability • Mutation Probability • Mutation Cell Divider • Cell Mutation Probability • Bit Mutation Probability

11. Neural Network Inputs • Current Parameter Values • Variance • Mean • Max fitness • Average bit changes for crossover • Constant parameters of the GA

12. Preliminary Results • Tests run with Knapsack problem with dataset 3, pop. size 800, rep. size 1600 • Learning Signal and Forgetting factor are not yet optimal enough to suggest better performance with NN

13. Output for 1600 generations

14. Probabilities for 1600 generations

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

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

17. Question ?