Designing Optimal Binary Search Tree Using Parallel Genetic Algorithms

Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]. Genetic algorithms (GAs) are developed as random search methods, which have not so sensitivity on primary data of the problems. They can be used in estimation of system parameters in order to obtain the best result. This can be achieved by optimization of an objective function. Genetic programming is a collection of methods for the automatic generation of computer programs that solve carefully specified problems, via the core, but highly abstracted principles of natural selection [12]. In this paper, genetic algorithms and parallel genetic algorithms have been discussed as one of the best solutions for optimization of the systems. Genetic and parallel genetic algorithms have been investigated in parallel programming environment called Multi-Pascal. Then an optimal binary search tree has been selected as a case study for decree sing of searching time. Also a dynamic programming method has been accelerated by using of a parallel genetic algorithm. In this case, by increasing the size of data, speed-up index will be increased.