Binary-like Real Coding Genetic Algorithm

Yongkang Lan
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Abstract

A new real coding genetic algorithm is proposed, which discretizes the continuous feasible region and then makes it continuous and complete by mutation operator and local search operator, thus achieving the uniformity of the discretization and continuity of the genetic algorithm. By comparison with binary genetic algorithm, differential evolution algorithm (DE), particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA), and artificial bee colony algorithm (ABC), the results show that the proposed algorithm outperforms the others in all test functions. The algorithm is applied to the case of optimizing the weights of neural networks and excellent results are obtained, which validates the effectiveness of the algorithm.
类二进制实编码遗传算法
提出了一种新的实数编码遗传算法,将连续可行域离散化,再通过变异算子和局部搜索算子使其连续完备,从而实现了遗传算法离散化和连续性的一致性。通过与二元遗传算法、差分进化算法(DE)、粒子群优化算法(PSO)、模拟退火算法(SA)和人工蜂群算法(ABC)的比较,结果表明该算法在所有测试功能上都优于其他算法。将该算法应用于神经网络权值优化的实例,取得了良好的效果,验证了算法的有效性。
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