Adaptive Heuristic

Adaptive heuristics are algorithms designed to dynamically adjust their search strategies in response to changing problem parameters or environments, aiming to improve efficiency and solution quality in complex optimization tasks. Current research focuses on integrating adaptive heuristics within metaheuristics (like differential evolution and CMA-ES), reinforcement learning frameworks, and fuzzy logic systems, often applied to diverse problems such as marketing optimization, multi-agent patrolling, and structural engineering design. These advancements enhance the robustness and adaptability of optimization algorithms, leading to improved performance in dynamic and unpredictable scenarios across various scientific and engineering domains.