Lexicase selection of specialists
Lexicase selection of specialists
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Lexcase 专家筛选
DOI：
10.1145/3321707.3321875
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
L. Spector
中科院分区：
文献类型：
--
作者：
Thomas Helmuth;Edward R. Pantridge;L. Spector
Lexicase 父选择通过一次考虑一个随机训练案例来过滤群体，消除当前案例中存在比选择池中最佳错误更差的错误的个体，直到留下一个个体。此过程通常在考虑所有训练案例之前停止，这意味着它将忽略尚未考虑的任何案例的错误值。因此，Lexicase 选择可以选择在某些训练案例上具有较小错误的专家个体，如果他们在其他训练案例上有很大的错误，并且这些错误接近用于所讨论的父选择事件的案例随机列表的开头。我们在这里假设，选择这些专家（可能具有较低的总错误率）在词典选择的观察到的性能优势中发挥着重要作用，优于错误聚合父选择方法（例如锦标赛选择），后者选择专家的频率要低得多。我们进行了实验来检验这一假设，并发现当我们剥夺词汇选择专家的能力时，它的性能和多样性维护会下降。这些发现有助于解释词典选择相对于锦标赛选择的性能改进，并表明专家帮助推动词典选择向全球解决方案的演变。
Lexicase parent selection filters the population by considering one random training case at a time, eliminating any individuals with errors for the current case that are worse than the best error in the selection pool, until a single individual remains. This process often stops before considering all training cases, meaning that it will ignore the error values on any cases that were not yet considered. Lexicase selection can therefore select specialist individuals that have poor errors on some training cases, if they have great errors on others and those errors come near the start of the random list of cases used for the parent selection event in question. We hypothesize here that selecting these specialists, which may have poor total error, plays an important role in lexicase selection's observed performance advantages over error-aggregating parent selection methods such as tournament selection, which select specialists much less frequently. We conduct experiments examining this hypothesis, and find that lexicase selection's performance and diversity maintenance degrade when we deprive it of the ability of selecting specialists. These findings help explain the improved performance of lexicase selection compared to tournament selection, and suggest that specialists help drive evolution under lexicase selection toward global solutions.