Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method

High accuracy and speed in data search, which are aims at finding the best solution to a problem, essential. This study examines brute force method, genetic algorithm, two proposed algorithms development of algorithm namely Multiple Crossover Genetic, Genetics with increments values. Brute is method direct approach solving problem based on formulation definition concepts involved. A search that uses evolution occurs living things as its basis. research selected case determining pin series by looking for match between target result. To test suitability 100-time tests were conducted each algorithm. The results this indicated has highest average generation rate 737146.3469 an time 1960.4296, latter gets score 36.78 0.0642.