We propose two general stopping criteria for finite length, simple genetic algorithms based on steady state distributions, and empirically investigate the impact of mutation rate, string length, crossover rate and population size on their convergence. Our first stopping criterion is based on the second largest eigenvalue of the genetic algorithm transition matrix, and the second stopping criterion is based on minorization conditions.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Modeling and Simulation
- Management Science and Operations Research
- Information Systems and Management