Genetic Algorithms: Potential Tools in Fitting Hearing Aids

4. Genes for possible solutions A population of genes evaluated in each iteration Fitness function Survival of the fittest: Elitism, Mutation, Cross-over Advantages-Disadvantages: Many solutions considered simultaneously. Inherent randomness: May help faster convergence and might prevent from converging to a local minimum. On the other hand, premature convergence or changes in search direction are possible. from abstract: search space is defined that includes all possible solutions and the peak of the surface represents the best solution. There is usually a well-defined error measure and the solutions are iteratively modified by minimizing the error. flexible: no need for continuity or differentiability conditions for the search space (like in gradient descent or simplex?)-also combining the best part from multiple solutions Gene: Every possible solution considered by the GA. A gene is a group of the parameters to be optimized. Fitness function: A measure for the quality of the genes in the population. In engineering applications, fitness can be determined by RMS error. In perception studies, the fitness is determined by the subjective input from the listener. Genes for possible solutions A population of genes evaluated in each iteration Fitness function Survival of the fittest: Elitism, Mutation, Cross-over Advantages-Disadvantages: Many solutions considered simultaneously. Inherent randomness: May help faster convergence and might prevent from converging to a local minimum. On the other hand, premature convergence or changes in search direction are possible. from abstract: search space is defined that includes all possible solutions and the peak of the surface represents the best solution. There is usually a well-defined error measure and the solutions are iteratively modified by minimizing the error. flexible: no need for continuity or differentiability conditions for the search space (like in gradient descent or simplex?)-also combining the best part from multiple solutions Gene: Every possible solution considered by the GA. A gene is a group of the parameters to be optimized. Fitness function: A measure for the quality of the genes in the population. In engineering applications, fitness can be determined by RMS error. In perception studies, the fitness is determined by the subjective input from the listener.

10. 3 sets of NR parameters: Slope, Offset, and Time Constant 5-6 paired comparisons in each iteration (10 iterations). GA Search Space: Offset: 30 linear steps TC: 11 log space Slope: 20 linear steps

11. 7-point scale A/B comparison between two genes. Subjects were asked to choose the sentence that was preferred (quality and comfort)

12. Compared average GA solutions with the best settings from the previous study (Exhaustive Listening) Slope: 95% accuracy Offset: 86% accuracy TC: 100% accuracy

13. Final Validation 7-point scale paired comparison between the following settings: Original signal, i.e., no NR. NR parameter settings identified previously (Exhaustive Listening). NR parameter settings produced by the GA.

15. Compared the GA average running time with Exhaustive Listening.

17. Conclusions

18. Conclusions (Cont�)

19. Conclusions (Cont�)

20. Thank you!