Existing stochastic selection strategies for parent selection in generational GA help build genetic diversity and sustain exploration; however, it ignores the possibility of exploiting knowledge gained by the process to make informed decisions for parent selection, which can often lead to an inefficient search for large, challenging optimization problems. This work proposes a deterministic parent selection strategy for recombination in a generational GA setting called Upper Bound-based Parent Selection (UBS) to solve NP-hard combinatorial optimization problems. Specifically, as part of the UBS strategy, we formulate the parent selection problem using the MAB framework and a modified UCB1 algorithm to manage exploration and exploitation. Further, we provided a unique similarity-based approach for transferring knowledge of the search progress between generations to accelerate the search. To demonstrate the effectiveness of the proposed UBS strategy in comparison to traditional stochastic selection strategies, we conduct experimental studies on two NP-hard combinatorial optimization problems: team orienteering and quadratic assignment. Specifically, we first perform a characterization study to determine the potential of UBS and the best configuration for all the selection strategies involved. Next, we run experiments using these best configurations as part of the comparison study. The results from the characterization studies reveal that UBS, in most cases, favors larger variations among the population between generations. Next, the comparison studies reveal that UBS can effectively search for high-quality solutions faster than traditional stochastic selection strategies on challenging NP-hard combinatorial optimization problems under given experimental conditions.

翻译：现有的代际遗传算法中用于父代选择的随机选择策略有助于建立遗传多样性并维持探索；然而，它忽略了利用过程中获得的知识来为父代选择做出明智决策的可能性，这通常会导致在解决大规模、具有挑战性的优化问题时搜索效率低下。本研究提出了一种用于代际遗传算法中重组的确定性父代选择策略，称为基于上界的父代选择（UBS），以解决NP难组合优化问题。具体而言，作为UBS策略的一部分，我们使用多臂赌博机（MAB）框架和一种改进的UCB1算法来构建父代选择问题，以管理探索与利用。此外，我们提出了一种独特的基于相似性的方法，用于在代际之间传递搜索进展的知识，以加速搜索。为了证明所提出的UBS策略相较于传统随机选择策略的有效性，我们在两个NP难组合优化问题上进行了实验研究：团队定向问题和二次分配问题。具体而言，我们首先进行了一项特性研究，以确定UBS的潜力以及所有相关选择策略的最佳配置。接下来，我们使用这些最佳配置进行实验，作为比较研究的一部分。特性研究的结果表明，在大多数情况下，UBS倾向于在代际之间产生更大的种群变异。随后，比较研究的结果显示，在给定的实验条件下，对于具有挑战性的NP难组合优化问题，UBS能够比传统的随机选择策略更有效地搜索到高质量解。