A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem

Recent years have witnessed a growing interest in automatic learning mechanisms and applications. The concept of hyper-heuristics, algorithms that either select among existing or generate new ones, holds high relevance this matter. Current research suggests that, under certain circumstances, hyper-heuristics outperform single heuristics when evaluated isolation. When are selected algorithms, they map problem states into suitable solvers. Unfortunately, identifying the features accurately describe state—and thus allow for proper mapping—requires plenty domain-specific knowledge, which is not always available. This work proposes simple yet effective hyper-heuristic model does rely on to produce such mapping. defines fixed sequence improves solving process knapsack problems. comprises an analysis feature-independent performance different conditions sets.