Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Finite Markov chain results in evolutionary computation: a tour d'horizon
Fundamenta Informaticae
Theory of evolution strategies - a tutorial
Theoretical aspects of evolutionary computing
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
Modeling and Analysis of Genetic Algorithm with Tournament Selection
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
Comparison of Certain Evolutionary Algorithms
Automation and Remote Control
"Go with the winners" algorithms
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Experimental comparison of two evolutionary algorithms for the independent set problem
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
| Hi-index | 5.23 |
In this paper, we study the conditions in which the random hill-climbing algorithm (1 + 1)-EA compares favorably to other evolutionary algorithms (EAs) in terms of fitness function distribution at a given iteration and with respect to the average optimization time. Our approach is applicable when the reproduction operator of an evolutionary algorithm is dominated by the mutation operator of the (1 + 1)-EA. In this case one can extend the lower bounds obtained for the expected optimization time of the (1 + 1)-EA to other EAs based on the dominated reproduction operator. This method is demonstrated on the sorting problem with HAM landscape and the exchange mutation operator. We consider several simple examples where the (1 + 1)-EA is the best possible search strategy in the class of the EAs.