Multi Expression Programming (MEP) is a Genetic Programming variant that uses a linear representation of chromosomes. MEP individuals are strings of genes encoding complex computer programs. When MEP individuals encode expressions, their representation is similar to the way in which compilers translate $C$ or $Pascal$ expressions into machine code. A unique MEP feature is the ability to store multiple solutions of a problem in a single chromosome. Usually, the best solution is chosen for fitness assignment. When solving symbolic regression or classification problems (or any other problems for which the training set is known before the problem is solved) MEP has the same complexity as other techniques storing a single solution in a chromosome (such as GP, CGP, GEP or GE). Evaluation of the expressions encoded into an MEP individual can be performed by a single parsing of the chromosome. Offspring obtained by crossover and mutation is always syntactically correct MEP individuals (computer programs). Thus, no extra processing for repairing newly obtained individuals is needed.
翻译：多表达式编程(MEP)是一种遗传方案变体,它使用一种染色体的线性表达方式。 MEP个人是基因编码复杂的计算机程序串。 当MEP个人编码表达式时, 他们的表示式与编译者将美元C$或美元Pascal$的表达式转换为机器代码的方式相似。 一个独特的MEP特征是能够将问题的多种解决方案存储在一个单一的染色体中。 通常, 最佳的解决方案是为健康分配选择。 当解决象征性回归或分类问题( 或在问题解决之前已知的培训组的其他任何问题) 时, MEP 与在染色体中存储单一解决方案的其他技术( 如 GP、 CGP、 GEP 或 GE) 一样复杂。 对已编码成MEP 个人表达式的表达式可以通过对染色体进行单一的分解来进行评估。 交叉和突变法获得的脱色体总是以同步方式纠正MEP个人( 计算机程序) 。 因此, 不需要对新获得的个人进行额外处理 。
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