This paper defines a new combinatorial optimization problem, namely General Combinatorial Optimization Problem (GCOP), whose decision variables are a set of parametric algorithmic components, i.e. algorithm design decisions. The solutions of GCOP, i.e. compositions of algorithmic components, thus represent different generic search algorithms. The objective of GCOP is to find the optimal algorithmic compositions for solving the given optimization problems. Solving the GCOP is thus equivalent to automatically designing the best algorithms for optimization problems. Despite recent advances, the evolutionary computation and optimization research communities are yet to embrace formal standards that underpin automated algorithm design. In this position paper, we establish GCOP as a new standard to define different search algorithms within one unified model. We demonstrate the new GCOP model to standardize various search algorithms as well as selection hyperheuristics. A taxonomy is defined to distinguish several widely used terminologies in automated algorithm design, namely automated algorithm composition, configuration and selection. We would like to encourage a new line of exciting research directions addressing several challenging research issues including algorithm generality, algorithm reusability, and automated algorithm design.

中文翻译：

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一般组合优化问题：走向自动化算法设计

本文定义了一个新的组合优化问题，即一般组合优化问题（GCOP），其决策变量是一组参数化算法组件，即算法设计决策。GCOP 的解决方案，即算法组件的组合，因此代表了不同的通用搜索算法。GCOP 的目标是找到解决给定优化问题的最佳算法组合。因此，求解 GCOP 相当于自动设计优化问题的最佳算法。尽管最近取得了进展，但进化计算和优化研究社区尚未接受支持自动化算法设计的正式标准。在这份立场文件中，我们将 GCOP 作为一种新标准，在一个统一模型中定义不同的搜索算法。我们展示了新的 GCOP 模型来标准化各种搜索算法以及选择超启发式。定义分类法是为了区分自动化算法设计中几个广泛使用的术语，即自动化算法组合、配置和选择。我们希望鼓励新的令人兴奋的研究方向，解决几个具有挑战性的研究问题，包括算法通用性、算法可重用性和自动化算法设计。配置和选择。我们希望鼓励新的令人兴奋的研究方向，解决几个具有挑战性的研究问题，包括算法通用性、算法可重用性和自动化算法设计。配置和选择。我们希望鼓励新的令人兴奋的研究方向，解决几个具有挑战性的研究问题，包括算法通用性、算法可重用性和自动化算法设计。