Death has long been overlooked in evolutionary algorithms. Recent research has shown that death (when applied properly) can benefit the overall fitness of a population and can outperform sub-sections of a population that are "immortal" when allowed to evolve together in an environment [1]. In this paper, we strive to experimentally determine whether death is an adapted trait and whether this adaptation can be used to enhance our implementations of conventional genetic algorithms. Using some of the most widely accepted evolutionary death and aging theories, we observed that senescent death (in various forms) can lower the total run-time of genetic algorithms, increase the optimality of a solution, and decrease the variance in an algorithm's performance. We believe that death-enhanced genetic algorithms can accomplish this through their unique ability to backtrack out of and/or avoid getting trapped in local optima altogether.
翻译:在进化算法中,死亡长期以来一直被忽视。最近的研究表明,死亡(如果应用得当)能够有利于人口的整体健康,并且能够超过允许在环境[1]中共同进化的“不死区”人群的分部门。 在本文中,我们努力尝试确定死亡是否是一种适应性特征,以及这种适应性是否可用于加强我们常规遗传算法的实施。我们运用了最广泛接受的进化死亡和老化理论,发现(各种形式的)白种死亡可以降低基因算法的总运行时间,提高解决方案的最佳性,并降低算法性表现的差异。 我们相信,死亡强化基因算法可以通过它们独特的能力来完成这一点,从而可以超越和(或)避免被完全困在本地的奥秘法中。