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提出了基于退化混沌突变算子的实数编码遗传算法 .此算法通过利用混沌特定的内在随机性、遍历性和变化的进化速率 ,较好地模拟了生物进化过程 ,提高了算法的爬山能力 ,并针对不同的进化阶段 ,自适应地采用不同的算子操作次序 ,在一定程度上保护了已得到的有效个体 .因此较好地克服了早熟收敛和停滞 ,并有效地解决了全局收敛性问题 .仿真结果表明 ,与已有的自适应算法相比 ,该算法容易实现 ,求解精度、收敛速度和可靠性较高 .
A real-coded genetic algorithm based on degraded chaos mutation operator is proposed.The algorithm optimizes the biological evolution process and improves the climbing ability of the algorithm by utilizing the intrinsic randomness, ergodicity and evolutionary rate of chaos, For different stages of evolution, adaptive operators adopt different order of operators and protect the effective individuals to a certain extent, so it overcomes premature convergence and stagnation well and effectively solves the global convergence problem. The simulation results show that compared with the existing adaptive algorithms, the algorithm is easy to implement, the solution accuracy, the convergence speed and the reliability are higher.