Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An approach to a problem in network design using genetic algorithms
An approach to a problem in network design using genetic algorithms
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Coding and Information Theory
'Adaptive Link Adjustment' Applied to the Fixed Charge Transportation Problem
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
The property analysis of evolutionary algorithms applied to spanning tree problems
Applied Intelligence
A memetic algorithm for the quadratic multiple container packing problem
Applied Intelligence
| Hi-index | 0.00 |
Evolution strategies (ES)are efficient optimization methods for continuous problems. However, many combinatorial optimization methods can not be represented by using continuous representations. The development of the network random key representation which represents trees by using real numbers allows one to use ES for combinatorial tree problems.In this paper we apply ES to tree problems using the network random key representation. We examine whether existing recommendations regarding optimal parameter settings for ES, which were developed for the easy sphere and corridor model, are also valid for the easy one-max tree problem.The results show that the 1/5-success rule for the (1+1)-ES results in low performance because the standard deviation is continuously reduced and we get early convergence. However, for the (碌+驴)-ES and the (碌, 驴)-ES the recommendations from the literature are confirmed for the parameters of mutation 驴1 and 驴2 and the ratio 碌/驴. This paper illustrates how existing theory about ES is helpful in finding good parameter settings for new problems like the one-max tree problem.