Novel Adaptive Genetic Algorithm Sample Consensus

TL;DR

This paper introduces an adaptive genetic algorithm for RANSAC that dynamically balances exploration and exploitation, leading to improved model fitting accuracy and efficiency in datasets with many outliers.

Contribution

It proposes a novel adaptive genetic operator that learns to balance exploration and exploitation during the optimization process.

Findings

Outperforms existing methods in inlier detection

Faster convergence to optimal models

Achieves higher accuracy in model fitting

Abstract

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.