The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Convergence Criteria for Genetic Algorithms
SIAM Journal on Computing
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
General cardinality genetic algorithms
Evolutionary Computation
Hi-index | 0.00 |
For a general Markov chain model of genetic algorithm, we establish an upper bound for the number of iterations which must be executed in order to generate, with a prescribed probability, a population consisting entirely of minimal solutions to a multiobjective optimization problem. However, since populations may contain multiple copies of the same element, we can only guarantee that at least one minimal solution is found. Using this upper bound, we then derive a stopping criterion which ensures that at least one minimal element is a member of the last population generated.