Novel adaptive genetic algorithm sample consensus

• Published in: Applied soft computing Vol. 77; pp. 635 - 642
• Main Authors: Shojaedini, Ehsan, Majd, Mahshid, Safabakhsh, Reza
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2019
• Subjects: Adaptive Random Sample Consensus; AGASAC; GASAC; Genetic algorithm; RANSAC; RANSAC; AGASAC; Adaptive Random Sample Consensus; GASAC; Genetic algorithm
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.01.052

Random Sample Consensus (RANSAC) is a successful algorithm in model fitting applications when there are numerous outliers within the dataset. Achieving a proper model is guaranteed through the pure exploration strategy of RANSAC. However, finding the optimum result requires exploitation. Genetic Algorithm Sample Consensus (GASAC) is an evolutionary paradigm which adds the exploitation capability to RANSAC. Although GASAC improves the results of RANSAC, it has a fixed strategy for balancing between exploration and exploitation. In this paper, a new paradigm is proposed based on genetic algorithms using an adaptive strategy. We propose an adaptive genetic operator to select the proper number of high fitness individuals as parents and mutate the rest. This operator can adjust the ratio of exploration vs. exploitation phases according to the amount of outliers. Also, a learning method is proposed for the mutation operator to gradually learn which gene is the best replacement for the mutated gene. This operator guides the exploration phase towards good solution areas and therefore produces better individuals for further exploitation. The proposed method is extensively evaluated in two sets of experiments. In all tests, our method outperformed the other methods in terms of both the number of inliers found and the speed of the algorithm. •An adaptive model fitting algorithm is proposed to handle different outlier rates.•The proposed method is an enhanced version of RANSAC which outperforms it.•A new method is presented to guide the exploration towards good solutions.•A selection operator is designed to tune the rate of exploration vs. exploitation.•A learning roulette wheel is proposed to gradually discriminate the outliers.