In a mirror server environment, clients request services from servers. Therefore, the system must have an intelligent algorithm to select the most suitable server to fulfill a coming request. Choosing such a server for a particular client may be very difficult. Evolutionary techniques can be utilized to determine the server best suited to a particular client request based on parameters such as processing and reply times. Usage of genetic algorithms in server selection is researched in this paper taking into consideration various probabilities for mutation and crossover.
Linear ordering problem is a well-known optimization problem attractive for its complexity (it is an NP-hard problem), rich library of test data and variety of real world applications. In this paper, we investigate the use and performance of two variants of genetic algorithms, mutation only genetic algorithms and higher level chromosome genetic algorithm, on the linear ordering problem. Both methods are tested and evaluated on a library of real world and artificial linear ordering problem instances.