In this paper a novel approach is described for tree visualization using nested circles. The brother nodes at the same level are represented by externally tangent circles; the tree nodes at different levels are displayed by using 2D nested circles or 3D nested cylinders. A new layout algorithm for tree structure is described. It provides a good overview for large data sets. It is easy to see all the branches and leaves of the tree. The new method has been applied to the visualization of file systems.

This paper describes a Genetic Algorithm (GA) convergence study for a highly multi-modal fitness function with non-ordered parameters. The measures of GA performance used are best single solution performance, effectiveness in finding the optimum and percentage of total search space (PTSS) covered. We developed several ways of adapting the crossover and mutation probabilities, and we compare the results of these methods with a canonical GA, a mutation-only GA, and the Srinivas' adaptive method. The results indicate that a large constant probability of crossover, regardless of the mutation method used does not provide high efficiency, for medium and large populations if covering a small PTSS. The most effective method while covering the smallest PTSS, is an adaptive mutation-only method. Our results suggest that when convergence speed is of utmost interest, for functions with non-ordered parameters mutation is more important than crossover despite massive multi-modality of the function optimized. Methods with adaptive crossover can, however, also give good results as long as mutation with a constant high probability is also performed.