To overcome the shortcoming of high computational cost of traditional interval optimization algorithms for high dimensional problems

an interval-genetic algorithm is presented that combines interval arithmetic and genetic algorithm.The algorithm has the advantages of simplicity and less knowledge about problems as traditional interval optimization algorithms.What is more

at each iteration the interval arithmetic provides the domains for the genetic algorithm to search

moreover

the genetic algorithm gives a direction to divide the reliable interval

and an upper bound of global optimum for a problem used to discard the intervals.Finally

a convergence is proved and numerical experiments show that the algorithm is more efficient than traditional interval optimization algorithms.