This paper introduces the real options approach, which is an evaluation tool for investment under uncertainty, to analyze optimal stopping time in genetic algorithms. This paper focuses on the simple model of EDAs named the compact genetic algorithm. This algorithm employs the probability vector as a model that scales well with the problem size. We analyze optimal stopping time of trap problems and propose an optimal stopping criterion as a decision contour. The proposed criterion also provides a stopping boundary, where termination is optimal on one side and continuation is on the other. This region suggests when it is worth continuing the algorithm and helps save computational effort by stopping early. Moreover, when the reset method is applied, the algorithm can reach a higher solution quality. The proposed technique can also be applied to analyze other problems.

journal homepage: www.elsevier.com/locate/asoc Real options approach to evaluating genetic algorithms