Implementation of histogram based sampling algorithm within an EDA scheme with CUDA

Tsutsui, Shigeyoshi; Fujimoto, Noriyuki

doi:10.1109/cec.2012.6256444

Cited by 2 publications

(1 citation statement)

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“…Not limited to the previous approaches, Estimation of Distribution Algorithms (EDAs) [32] have also been used to solve the QAP. For instance, in [52,43,49] EDA-based algorithms were proposed. In this paper, we propose a new EDA with properties that are specially suited to deal with the QPA.…”

Section: Introductionmentioning

confidence: 99%

Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem

Arza,

Perez,

Irurozki

et al. 2019

Preprint

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The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard problem represents, it has captured the attention of the evolutionary computation community for decades. As a result, a large number of algorithms have been proposed to optimize this algorithm. Among these, exact methods are only able to solve instances of size n < 40, and thus, many heuristic and metaheuristic methods have been applied to the QAP.In this work, we follow this direction by approaching the QAP through Estimation of Distribution Algorithms (EDAs). Particularly, a non-parametric distance-based exponential probabilistic model is used. Based on the analysis of the characteristics of the QAP, and previous work in the area, we introduce Kernels of Mallows Model under the Hamming distance to the context of EDAs. Conducted experiments point out that the performance of the proposed algorithm in the QAP is superior to (i) the classical EDAs adapted to deal with the QAP, and also (ii) to the specific EDAs proposed in the literature to deal with permutation problems.

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Section: Introductionmentioning

confidence: 99%