X.-J. Jing and Y.-C. Wang (PRC)
Genetic algorithm, genetic information, convergence, rational genetic algorithm
By using the feedback of genetic information and heuristic rules, and incorporating local searching algorithms, rational genetic algorithm (RGA) is proposed to overcome the drawbacks of conventional genetic algorithms (GAs) such as slow convergence. Genetic Information was defined, which is the feed-back information derived from evolutionary process to supervise GA’s operations. Furthermore, heuristic rules and local searching algorithms were also effectively incorporated in RGA to enhance the correctness of genetic operations. Finally, a general specification for the whole RGA was provided. RGA effectively incorporates inheriting and learning behaviors of knowledge and experiences in species into conventional GA. From the theoretical analysis of RGA and case studies in practical application to path planning problems of robots, it can be seen that the proposed RGA has faster convergence speed, and can converge to the global optimal solution.
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