Using Genetic Algorithms with Real-coded Binary Representation for Solving Non-stationary Problems

Abstract

This paper presents genetic algorithms with real-coded binary representation - a novel approach to improve the performance of genetic algorithms. The algorithm is capable of maintaining the diversity of the evolved population during the whole run which protects it from the premature convergence. This is achieved by using a special encoding scheme, introducing a high redundancy, which is further supported by the so-called gene-strength adaptation mechanism for controlling the diversity. The mechanism for the population diversity self-regulation increases the robustness of the algorithm when solving non-stationary problems as was empirically proven on two test cases. The achieved results show the competitiveness of the proposed algorithm with other techniques designed for solving non-stationary problems.