Search algorithms can be used to solve discrete optimization problems (DOPs), a class of computationally expensive problems with significant theoretical and practical interest. Search algorithms solve DOPs by evaluating candidate solutions from a finite or countably infinite set of possible solutions to find one that satisfies a problem-specific criterion. DOPs are also referred to as combinatorial problems.

A discrete optimization problem can be expressed as a tuple (*S*, *f*). The set *S* is a finite or countably infinite set of all solutions that satisfy specified constraints. This set is called the set of feasible solutions. The function *f* is the cost function ...

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