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• W1979809380 abstract "The quadratic assignment problem (QAP) is one of the most studied NP-hard problems with various practical applications. In this work, we propose a powerful population-based memetic algorithm (called BMA) for QAP. BMA integrates an effective local optimization algorithm called Breakout Local Search (BLS) within the evolutionary computing framework which itself is based on a uniform crossover, a fitness-based pool updating strategy and an adaptive mutation procedure. Extensive computational studies on the set of 135 well-known benchmark instances from the QAPLIB revealed that the proposed algorithm is able to attain the best-known results for 133 instances and thus competes very favorably with the current most effective QAP approaches. A study of the search landscape and crossover operators is also proposed to shed light on the behavior of the algorithm." @default.
• W1979809380 created "2016-06-24" @default.
• W1979809380 creator A5015903546 @default.
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• W1979809380 date "2015-01-01" @default.
• W1979809380 modified "2023-10-07" @default.
• W1979809380 title "Memetic search for the quadratic assignment problem" @default.
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• W1979809380 doi "https://doi.org/10.1016/j.eswa.2014.08.011" @default.
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