A novel robust statistical design of the repeated genetic algorithm

Abstract

The genetic algorithm is a simple optimization method for a wide variety of computer vision problems. However, its performance is often brittle and degrades drastically with increasing input problem complexity. While this problem is difficult to overcome due to the stochastic nature of the algorithm, this paper shows that a robust statistical design using repeated independent trials and hypothesis testing can be used to greatly alleviate the degradation. The working principle is as follows: The probability of success P of a stochastic algorithm A (genetic algorithm) can be estimated by running N copies of A simultaneously or running A repeatedly N times. By hypothesis testing, it is shown that P can be estimated to a required figure of merit (i.e. the level of significance). Knowing P, the overall probability of success Prepeated for N applications of A can be computed. This is used in turn to adjust N in an iterative scheme to maintain a constant Prepeated, achieving a robust feedback loop. Experimental results are reported on the application of this novel algorithm to an affine object detection problem.