Novel Adaptive Genetic Algorithm Sample Consensus
This work addresses a specific bottleneck in model fitting algorithms for computer vision or similar domains, offering an incremental improvement over existing methods like GASAC.
The paper tackles the problem of balancing exploration and exploitation in model fitting by proposing a novel adaptive genetic algorithm sample consensus method, which outperforms existing methods in both the number of inliers found and algorithm speed across all tests.
Random sample consensus (RANSAC) is a successful algorithm in model fitting applications. It is vital to have strong exploration phase when there are an enormous amount of outliers within the dataset. Achieving a proper model is guaranteed by pure exploration strategy of RANSAC. However, finding the optimum result requires exploitation. GASAC is an evolutionary paradigm to add exploitation capability to the algorithm. 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 algorithm with an adaptive strategy. We utilize an adaptive genetic operator to select high fitness individuals as parents and mutate low fitness ones. In the mutation phase, a training method is used to gradually learn which gene is the best replacement for the mutated gene. The proposed method adaptively balance between exploration and exploitation by learning about genes. During the final Iterations, the algorithm draws on this information to improve the final results. The proposed method is extensively evaluated on two set 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.