用mNM-landscapes和Boltzmann分布计算Pareto阵面的分解近似
Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions
摘要：
NM景观最近被引入作为一类可调的坚固模型。它们是一般交互作用模型的子集，其中所有交互作用的阶数小于或等于$M$。玻尔兹曼分布已被广泛应用于单目标进化算法中，以实现选择和研究建模算法的理论性质。在本文中，我们提出了多目标NM景观模型和玻尔兹曼分布的组合，以获得帕累托前沿近似。我们调查的NM景观的参数和概率因子分解的帕累托前近似的形状的联合效果。
英文摘要：
NM-landscapes have been recently introduced as a class of tunable rugged models. They are a subset of the general interaction models where all the interactions are of order less or equal $M$. The Boltzmann distribution has been extensively applied in single-objective evolutionary algorithms to implement selection and study the theoretical properties of model-building algorithms. In this paper we propose the combination of the multi-objective NM-landscape model and the Boltzmann distribution to obtain Pareto-front approximations. We investigate the joint effect of the parameters of the NM-landscapes and the probabilistic factorizations in the shape of the Pareto front approximations.
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