Many data-limited fish stocks worldwide require management advice. Simple empirical management procedures have been used to manage data-limited fisheries but do not necessarily ensure compliance with maximum sustainable yield objectives and precautionary principles. Genetic algorithms are efficient optimization procedures for which the objectives are formalized as a fitness function. This optimization can be included when testing management procedures in a management strategy evaluation. This study explored the application of a genetic algorithm to an empirical catch rule and found that this approach could substantially improve the performance of the catch rule. The optimized parameterization and the magnitude of the improvement were dependent on the specific stock, stock status, and definition of the fitness function. The genetic algorithm proved to be an efficient and automated method for tuning the catch rule and removed the need for manual intervention during the optimization process. Therefore, we conclude that the approach could also be applied to other management procedures, case-specific tuning, and even data-rich stocks. Finally, we recommend the phasing out of the current generic ICES “2 over 3” advice rule in favour of case-specific catch rules of the form tested here, although we caution that neither works well for fast-growing stocks.

中文翻译：

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使用遗传算法优化数据有限的捕获规则

全球许多数据有限的鱼类种群需要管理建议。简单的经验管理程序已用于管理数据有限的渔业，但不一定确保遵守最大可持续产量目标和预防原则。遗传算法是有效的优化程序，其目标被形式化为适应度函数。当在管理策略评估中测试管理程序时，可以包括这种优化。本研究探索了遗传算法在经验捕获规则中的应用，发现这种方法可以显着提高捕获规则的性能。优化的参数化和改进的幅度取决于特定的库存、库存状态和适应度函数的定义。遗传算法被证明是调整捕获规则的一种有效且自动化的方法，并且在优化过程中无需人工干预。因此，我们得出结论，该方法也可以应用于其他管理程序、特定案例的调整，甚至是数据丰富的股票。最后，我们建议逐步淘汰当前通用的 ICES“​​2 比 3”建议规则，转而采用此处测试形式的特定案例捕获规则，尽管我们警告说，这两种规则都不适用于快速增长的股票。