relational databases query optimization using hybrid evolutionary algorithm

optimizing the database queries is one of hard research problems. exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. the use of evolutionary methods, because of their efficiency and strength, has been changed in to a suitable research area in the field of optimizing the database queries. in this paper, a hybrid evolutionary algorithm has been proposed for solving the optimization of join ordering problem in database queries. this algorithm uses two methods of genetic algorithm and learning automata synchronically for searching the states space of problem. it has been showed in this paper that by synchronic use of learning automata and genetic algorithms in searching process, the speed of finding an answer has been accelerated and prevented from getting stuck in local minimums. the results of experiments show that hybrid algorithm has dominance over the methods of genetic algorithm and learning automata