中文说明：三种方法被提出，以指导遗传算法的收敛向所需的解决方案：偏压以便有利于搜索空间的特定区域的探索初始群体的产生，根据归因不同的权重的目标和比较解的目标的加权和（WPMOGA），并且包括最小和最大的交易客中的解决方案（G-MOGA）的比较。三种方法进行，以发现是否意味着增加一些新的传输线的解决方案上测试的网络的扩展的问题。加权和的方法似乎是低效率的，而其他两个成功引导随机群体朝少添加的行的解决方案。偏压初始种群是最简单的实施而所提供的最佳结果，但容易使用该方法的依赖于所考虑的问题，并且它可能不是可实现的，在大多数的多目标的问题。反之，在G-MOGA是通用的，可以在任何类型的多目标的问题予以实施。结果被通过的可靠性核心措施，这表明在网络中的节点的相对重要性装置根据连接节点的，程度进行了分析，其接近到网络的其他节点，其中最可靠的存在网络的路径，并除去该节点到网络的可靠性的效果。
English Description：
Three methods were proposed to guide the convergence of the genetic algorithm towards desired solutions: biasing the generation of the initial population in order to favour the exploration of a certain region of the search space, attributing different weights to the objectives and comparing the solutions according to the weighted sum of the objectives (WPMOGA), and including minimum and maximum trades-off in the comparison of solutions (G-MOGA). The three methods were tested on the network expansion problem in order to find solutions that imply adding few new transmission lines. The weighted sum method appeared to be inefficient, while the two other succeeded in guiding the random population towards solutions with few added lines. Biasing the initial population was the simplest to implement and furnished the best results, but the ease of using this method depend on the problem considered, and it might not be implementable in most multi-objectives problems. Contrariwise, the G-MOGA is u