Research Resources

This section shares benchmarking and related research information from inside Starkey Hearing Technologies, including detailed methodology used in data collection.

Starkey Genetic Algorithm Toolbox for MATLAB

Overview

This toolbox allows Starkey's genetic algorithm (GA) to be used to find high quality solutions to multivariable listening problems. A genetic algorithm is inspired by evolutionary genetics, in which better solutions are found over time using a combination of mutation (random search) and crossover (combining properties of previous solutions). The toolbox is designed with perceptual applications in mind (i.e., where a human subject judges outputs controlled by parameters such as time constants, gains, and filter lengths for audio processing devices). A major component of the Starkey GA is that its input is a series of paired comparison preference judgments. Although The MathWorks sells the "Genetic Algorithm and Direct Search Toolbox 2.1," it is not targeted towards perceptual applications and does not include a method of using subjective judgments from a human observer as input.

The toolbox has been tested with and is designed for compatibility with MATLAB 7.0 and 7.4. It does not require any other toolboxes. Due to breaking changes in MATLAB, it is unlikely that it will work with earlier versions, and it is uncertain whether it will work with later versions without additional testing and modification.

The interface of the toolbox is modeled after existing MATLAB optimization functions, such as fminsearch. For example, the GA is executed by calling the function ga and providing it with a function to optimize and a configuration object created by gaoptimset. The toolbox includes example code, including the file simscript.m, which shows a variety of ways to invoke and configure the GA. For a quick start, try the configuration GUI, gaoptimsetDialog, which is described in detail below.

The single required item in the configuration object is Parameters, which is a cell array of numeric vectors specifying the ordered, valid values for each parameter to be optimized by the GA. There must be at least one parameter (i.e., at least one vector in the cell array), but typical experiments have between two and six parameters. Varying more than this is not recommended without giving careful consideration to the population size and number of comparisons required.

Comparison Function

Configuration GUI

Summary of gaoptimset Parameters

Instructions for getting the Starkey GA Toolbox

References

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