Differential Evolution (DE) algorithm is an important Evolutionary Algorithm (EA) for global optimization over continuous spaces, which can also work with discrete variables. The success of DE in solving a specific problem is closely related to appropriately choosing its control parameters, in this context, self-adaptation allows the algorithm to reconfigure itself, automatically adapting to the problem being solved. In self-adaptation the control parameters are encoded into the genotype of the individuals and undergo the actions of variation operators. In the literature, there are several different operators proposed to vary the encoded parameters, however, there is a lack of information about their influence on the algorithms performance. To cover part of this lack of knowledge, in this paper a comparison of variation operators, commonly used to adapt parameters in self-adaptive versions of DE, is presented. The experiments on well know benchmark functions indicates that operators which maintain the control parameters diversity work better than the others.
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机译：差分演进（DE）算法是一种重要的进化算法（EA），用于连续空间的全局优化，也可以使用离散变量。 The success of DE in solving a specific problem is closely related to appropriately choosing its control parameters, in this context, self-adaptation allows the algorithm to reconfigure itself, automatically adapting to the problem being solved.在自适应中，控制参数被编码为个体的基因型，并经历变化运算符的动作。在文献中，有几个不同的运算符建议改变编码参数，然而，缺乏关于它们对算法性能的影响的信息。为了涵盖这种缺乏知识的一部分，在本文中，呈现了变化运算符的比较，通常用于适应自适应版本的DE中的参数。众所周知的基准函数的实验表明，维护控制参数多样性的运算符优于其他运算符。
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