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NK-landscapes as test functions for evaluation of host-parasite algorithms

机译:nk-landscapes作为评估宿主寄生虫算法的测试功能

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Previous research on host-parasite algorithms has shown that the co-evolutionary arms race is difficult to sustain when the tasks faced by hosts and parasites are heavily asymmetric. We have therefore proposed an asymmetry-handling algorithm, AHPA, with a capacity for self-adapting the allocation of generations to hosts and parasites, so that the problem asymmetry is counteracted. In this paper we discuss the need for systematic evaluation of this algorithm, so that its behaviour under varying levels of asymmetry can be studied in detail. We propose the use of Kaufmann's NK landscape model for this purpose, and show how the model can be adapted for the testing of host-parasite algorithms. Using the adapted model, we present simulation results which confirm AHPA's ability to sustain a stable arms-race under varying levels of asymmetry.
机译:以前关于宿主寄生虫算法的研究表明,当宿主和寄生虫面对的任务严重不对称时,共同进化的军备竞赛难以维持。因此,我们提出了一种不对称处理算法,AHPA,具有自适应对托管和寄生虫的代工分配的能力,因此抵消了问题不对称性。在本文中,我们讨论了对该算法进行系统评估的需求,从而可以详细研究其在不同不对称水平下的行为。我们提出了为此目的使用Kaufmann的NK横向模型,并展示了如何适应对宿主寄生虫算法的测试。使用适应的模型,我们存在模拟结果,该结果证实了AHPA在不同水平的不对称水平下维持稳定的武器种族的能力。

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