Model-based evolutionary algorithms: a short survey
Model-based evolutionary algorithms: a short survey
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DOI：
10.1007/s40747-018-0080-1
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发表时间：
2018-12-01
影响因子：
5.8
通讯作者：
Yao, Xin
中科院分区：
文献类型：
--
作者：
Cheng, Ran;He, Cheng;Yao, Xin
进化算法是一类受自然启发的算法，广泛用于解决复杂的优化问题。由于大多数传统进化算法中的算子(如交叉、变异、选择)都是基于固定的启发式规则或策略来开发的，因此它们无法学习待优化问题的结构或性质。为了使进化算法具有学习能力，最近提出了各种基于模型的进化算法。这项调查通过考虑三种不同的使用模式的动机，简要回顾了一些有代表性的MBA学生。首先，使用模型最常见的动机是估计候选解决方案的分布。其次，在进化多目标优化中，使用模型的动机之一是建立从目标空间到决策空间的逆模型。第三，在解决计算代价较高的问题时，可以使用模型作为适应度函数的代理。在综述的基础上，还提出了进一步的讨论。
The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given.