Zamdborg Leonid, Holloway David M, Merelo Juan J, Levchenko Vladimir F, Spirov Alexander V
Department of Applied Mathematics and Statistics, The Stony Brook State University, NY.
Mathematics Department, British Columbia Institute of Technology, 3700 Willingdon Avenue, Burnaby, B.C., Canada, V5G 3H2.
Inf Sci (N Y). 2015 Jun 10;306:88-110. doi: 10.1016/j.ins.2015.02.012.
Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. Their demonstrated efficacy has reawakened an interest in other aspects of contemporary biology as an inspiration for new algorithms. However, amongst the many excellent candidates for study, contemporary models of biological macroevolution attract special attention. 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. Many evolutionary biologists posit that it is the co-evolution of populations with their genomic parasites that permits the high efficiency of evolutionary searches found in the living world. 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. This navigation problem is widely known as a classical benchmark test and possesses a large body of literature. We add new objects to the standard toolkit of GA - artificial transposons and a collection of operators that operate on them. We define these artificial transposons as a fragment of an ant's code with properties that cause it to stand apart from the rest. The minimal set of operators for transposons is a transposon mutation operator, and a transposon reproduction operator that causes a transposon to multiply within the population of hosts. An analysis of the population dynamics of transposons within the course of ant evolution showed that transposons are involved in the processes of propagation and selection of blocks of ant navigation programs. During this time, the speed of evolutionary search increases significantly. 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.
现代进化计算利用基于从达尔文自然选择理论借鉴的概念的启发式优化方法。它们已证明的有效性重新唤起了人们对当代生物学其他方面的兴趣，将其作为新算法的灵感来源。然而，在众多优秀的研究候选对象中，当代生物宏观进化模型引起了特别关注。我们认为，该领域的一个重要方向必须是对生物进化中“基因组寄生虫”（如转座子）活动进行建模的算法。许多进化生物学家认为，正是种群与其基因组寄生虫的共同进化使得生物界中进化搜索具有高效率。本出版物是我们朝着开发一组最少的模拟基因组寄生虫作用的算法迈出的第一步。具体而言，我们从遗传算法（GA）领域入手，并选择人工蚂蚁问题作为测试案例。这个导航问题作为经典的基准测试广为人知，并且有大量的文献。我们在GA的标准工具包中添加了新对象——人工转座子以及对它们进行操作的一组算子。我们将这些人工转座子定义为蚂蚁代码的一个片段，其特性使其与其他部分区分开来。转座子的最少算子集是一个转座子突变算子，以及一个使转座子在宿主种群中繁殖的转座子繁殖算子。对蚂蚁进化过程中转座子种群动态的分析表明，转座子参与了蚂蚁导航程序块的传播和选择过程。在此期间，进化搜索的速度显著提高。我们得出结论，类似于真实转座子，人工转座子在与其宿主共同进化的过程中，确实能够作为智能变异体，根据进化问题进行适应性变化。
