Recognition of the underwater target with an improved genetic-based classifier system

Abstract

Due to the complicated watery environment and the limitation of the detecting method to the underwater target, recognizing them precisely is always a hard problem to all countries. In this paper, we design a genetic-based classifier system (CS) and apply it to recognize the underwater target. This is an attempt to solve this problem with machine learning way. Compared with traditional CS, the proposed Comparing and Matching Algorithm will give the fitness value more explicit statistical meaning, which will make us easier to explain the rules with background knowledge. The proposed Hyperplasia Operator can handle those instances which are not emerged before. It gives the system persistent learning abilities, so the system may be more compatible with the surroundings. The proposed Refining Classifier merges those redundant rules and shrinks the rule set. In addition, we give and discuss an alterable mutation probability to the genetic algorithm in the CS, which increases the speed and the accuracy of the classifying operation. At last, the experiments to examine the system with the sample data which are collected from sonar echo samples. Experiments show that the recognizing results are satisfying.