Genetic Algorithms (GAs) are adaptive search methods, which try to incorporate the principle of surviving known from nature. They proved to be an efficient instrument for solving many hard problems in different areas, in which the majority of other techniques failed as being weak or not applicable. On the other hand, GAs fight with a number of problems, as well. To the crucial issues belong the representation of potential solutions in the search space, design of the proper operators that drive the search and the configuration of the routines and strategies used in the GAs. This paper presents some results of our research on GAs. The interesting observations encountered by experiments concerning the initialisation of GAs’ runs and by the enhanced crossover operator for binary chromosomes are presented. We have used a shell calledGAToolfor automatic experimenting with GAs, which was developed for GAs’ performance evaluation. The representatives of Travelling Salesman Problem (TSP), Continuous functions and Deceptive functions, which are the usual benchmarks mentioned in many studies, were used in our experiments. ©1999 American Institute of Physics.