Reinforced Genetic Algorithm using Clustering based on Statistical Estimation
Year: 2018
Type: article
Abstract: Genetic algorithm(GA) has beend widely used to obtain solution in various optimization problems because of its robustness and convergence property. GA algorithm, however, has limitations; the computational time increases sharply as the complexity of the problem increases and the user’s arbitrary judgement is involved especially in its termination step. In order to solve these limitations, we suggest a modified reinforced GA using clustering based on the statistical estimation. In this method, the mathematical reliability is gradually increased for each generation to find the solution vector to be finally obtained. The similarity between each solution vector generated by GA is determined and the inefficient repetitive calculation is remarkably reduced. In addition, the statistical reliability of the obtained solution vectors can be calculated to reduce the randomness of the user in the conventional termination step.
Source: IFAC-PapersOnLine
Authors: Taekyoon Park, Yeonsoo Kim, Jong Min Lee
Institution: Seoul National University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2897992797