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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.
Qu et al. (Mon,) studied this question.