摘要翻译：
本文的目的是在一个基于状态的进化算法(SEA)的实例中分析不同编码转换算子的表型效应（进化能力）。由于优化过程中解的表示或最佳编码的选择对进化算法的效率非常重要，我们将讨论一种耦合多个表示的策略以及在搜索过程中从一种编码到另一种编码的不同转换过程。在其他地方，一些EA试图使用多个表示(SM-GA、SEA等），意图从每一个表示中受益。尽管如此，本文表明，在试图提高这类EAS的性能时，改变表示形式也是一个重要的考虑因素。作为一个演示例子，我们使用了具有两个相同搜索空间但不同编码转换算子的两态SEA(2-SEA)。结果表明，从一种编码到另一种编码的转换方式，无论是最佳表示的选择还是表示本身都是非常有利的，必须考虑到这一点，才能更好地解决和提高EAs的执行。
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英文标题：
《Do not Choose Representation just Change: An Experimental Study in
  States based EA》
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作者：
Maroun Bercachi (I3S), Philippe Collard (I3S), Manuel Clergue (I3S),
  Sebastien Verel (I3S)
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Our aim in this paper is to analyse the phenotypic effects (evolvability) of diverse coding conversion operators in an instance of the states based evolutionary algorithm (SEA). Since the representation of solutions or the selection of the best encoding during the optimization process has been proved to be very important for the efficiency of evolutionary algorithms (EAs), we will discuss a strategy of coupling more than one representation and different procedures of conversion from one coding to another during the search. Elsewhere, some EAs try to use multiple representations (SM-GA, SEA, etc.) in intention to benefit from the characteristics of each of them. In spite of those results, this paper shows that the change of the representation is also a crucial approach to take into consideration while attempting to increase the performances of such EAs. As a demonstrative example, we use a two states SEA (2-SEA) which has two identical search spaces but different coding conversion operators. The results show that the way of changing from one coding to another and not only the choice of the best representation nor the representation itself is very advantageous and must be taken into account in order to well-desing and improve EAs execution.
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PDF链接：
https://arxiv.org/pdf/0905.2882