A new approach to avoiding the local extrema trap

Abstract

The Extremum Consistency algorithm avoids local maxima and minima in a specialised domain. The most notable difference between this approach and others is that it places a greater importance on the width or consistency of an extremum than on its height or depth (amplitude). Short term, high amplitude extrema are encountered in many typical situations (such as noisy environments or due to hardware inaccuracies) and cause problems with system accuracy. The Extremum Consistency algorithm is far less susceptible to these situations than hill climbing, convolution, thresholding, and tends to produce higher quality results. We describes the algorithm and present results from practical experimentation, which illustrates its superiority over other forms of local extrema avoidance in three real world applications.