| Abstract [eng] |
The use of genetic algorithms considerably increases. In some research works GA‘s are investigated to optimize graph problems. There are many different strategies for GA optimization. Unfortunately, there are no investigations if a strategy, suitable for a particular graph problem, will be useful solving other graph problems. In this work I originated, described and developed some GA learning strategy elements. Also I developed some that are available in other research works. These elements are: generation of initial population, selection of individuals, mutation, crossover and some other parameters. All possible strategies (about 300) are tested in this work for three graph problems: shortest path, longest path and traveling salesman problem. Results are summarized and described. |
