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Acceptable solution

As explained in Grokking AI Algorithms, Second Edition

An acceptable solution is a candidate solution whose quality is sufficient for the requirements and use case, even if it is not optimal.

An acceptable solution is good enough for the requirements and use case, even when it is not the optimal solution. Whether “good enough” works depends on the consequences of being imperfect. section 4.2

Judge acceptability by considering:

  • Requirements: the candidate must satisfy the problem’s constraints. section 4.2
  • Fitness: a higher-value answer may be preferable even when finding it takes time. section 4.2
  • Use case: a good-enough recommendation may be fine, while a mission-critical health system may require more certainty. section 4.2

Choosing items for a knapsack

A knapsack must stay within a weight limit while maximizing item value. With many items, checking every combination becomes expensive, so an evolutionary algorithm may return a high-value combination without guaranteeing the single best one. That combination is acceptable if it meets the use case’s requirements. section 4.2

How Grokking AI Algorithms, Second Edition covers it

How deep it goes
Defines acceptability through constraints, fitness, and use-case consequences without developing a formal acceptance test.
Examples
Uses small and large knapsack datasets to contrast optimal search with computationally practical results.
What it assumes
Assumes basic familiarity with solutions, constraints, optimization, and the distinction between optimal and acceptable results.
What you can do after
Evaluate whether a nonoptimal result is sufficient by comparing its fitness, constraints, computational cost, and use-case risk.
How it connects
Connects acceptable solutions to evolutionary algorithms, the knapsack problem, brute-force algorithms, computational efficiency, and solution fitness.
Cover of Grokking AI Algorithms, Second Edition
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