Optimize Velocity of Gene Function Prediction by Using Greedy-Genetic Algorithm on Computational Sequence Variants

The point which makes the Genetic Algorithm so robust in many kinds of complex situations is its flexibility to generate many populations by employing the crossover and mutation techniques. This is shown in solving a difficult problem like the computational DNA sequence variant which can be easily solved by applying a simple Genetic Algorithm. However, achieving a fast and optimized algorithm to generate any order of reproduction is a matter of time and cost. The main contribution of this paper is on the optimization in the selection stage of the Genetic Algorithm (GA) that proposes to build a new hybrid of the Greedy and the Genetic Algorithms for the computation of DNA sequences variant. This is achieved by integrate Greedy and Genetic algorithm, aim to pick up the most suitable parents on selection segment. Sequentially, fitness function combined with greedy algorithm is applied to reduce the number of node within the network and optimize the velocity of selection segment through genetic algorithm. In comparison with other well-known algorithm, this method optimized the complexity into O(nlogn).