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原文連結
論文資訊
- 類型：已發表論文
- 日期：2018-06-12
摘要
The traditional way of tackling discrete optimization problems is by using local search on suitably defined cost or 適應度景觀s. Such approaches are however limited by the slowing down that occurs when the local minima that are a feature of the typically rugged landscapes encountered arrest the progress of the search process. Another way of tackling optimization problems is by the use of heuristic approximations to estimate a global cost minimum. Here, we present a combination of these two approaches by using cover-encoding maps which map processes from a larger search space to subsets of the original search space. The key idea is to construct cover-encoding maps with the help of suitable heuristics that single out near-optimal solutions and result in landscapes on the larger search space that
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