Average Edit Distance Bacterial Mutation Algorithm for effective optimisation

摘要

In the field of Evolutionary Computation (EC), many algorithms have been proposed to enhance the optimisation search performance in NP-Hard problems. Recently, EC research trends have focused on memetic algorithms that combine local and global optimisation search. One of the state-of-the-art memetic EC methods named Bacterial Memetic Algorithm (BMA) has given good optimisation results. In this paper, the objective is to improve the existing BMA optimisation performance without significant impact to its processing cost. Therefore, we propose a novel algorithm called Average Edit Distance Bacterial Mutation (AEDBM) algorithm that improves the bacterial mutation operator in BMA. The AEDBM algorithm performs edit distance similarity comparisons for each selected mutation elements with other bacterial clones before assigning the selected elements to the clones. In this way, AEDBM will minimise bad (similar elements) bacterial mutation to other bacterial clones and thus improve the overall optimisation performance. We investigate the proposed AEDBM algorithm on commonly used datasets in fuzzy logic system analysis. We also apply the proposed method to train a robotic learning agent's perception-action mapping dataset. Experimental results show that the proposed AEDBM approach in most cases gains consistent mean square error optimisation performance improvements over the benchmark approach with only minimal impact to processing cost.