摘要翻译：
现代进化计算利用了基于达尔文自然选择理论的启发式优化。我们认为，该领域的一个重要方向必须是模拟基因组寄生虫（如转座子）在生物进化中的活动的算法。这份出版物是我们在开发模拟基因组寄生虫角色的最小分类算法的方向上迈出的第一步。具体来说，我们从遗传算法领域入手，选择人工蚂蚁问题作为测试用例。我们将这些人工转座子定义为蚂蚁代码的一个片段，它具有使其与众不同的特性。我们得出结论，人工转座子类似于真实转座子，确实能够作为智能变异体，在与宿主共同进化的过程中适应进化问题。
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英文标题：
《Forced Evolution in Silico by Artificial Transposons and their Genetic
  Operators: The John Muir Ant Problem》
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作者：
Alexander V. Spirov, Alexander B. Kazansky, Leonid Zamdborg, Juan J.
  Merelo and Vladimir F. Levchenko
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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：
  Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. We believe that a vital direction in this field must be algorithms that model the activity of genomic parasites, such as transposons, in biological evolution. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. We define these artificial transposons as a fragment of an ant's code that possesses properties that cause it to stand apart from the rest. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts.
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PDF链接：
https://arxiv.org/pdf/0910.5542