Burke D S, De Jong K A, Grefenstette J J, Ramsey C L, Wu A S
Center for Immunization Research, School of Hygiene and Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
Evol Comput. 1998 Winter;6(4):387-410.
The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve as scratch space in which VIV can explore alternative gene values. These results represent a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.
当前的大多数遗传算法（GA）虽然受到自然进化系统的启发，但很少被视为具有生物学合理性的模型。这并非对遗传算法的批评，而是反映了在对生物机制进行建模时所选择的抽象层次，以及进化计算领域更侧重于工程的目标。更好地理解并缩小遗传算法与遗传学之间的差距，一直是一个跨学科项目的核心问题，该项目的目标是构建基于遗传算法的病毒进化计算模型。其成果是一个名为虚拟病毒（VIV）的系统。VIV纳入了许多更具生物学合理性的机制，包括更灵活的基因型到表型映射。在VIV中，基因与位置无关，基因组长度可以变化，可能包含非编码区以及重复或竞争基因。对VIV进行的初步计算研究已经揭示了一些具有生物学和计算学意义的涌现现象。在没有基于基因组长度的任何惩罚的情况下，VIV会产生具有长基因组的个体，并且（从解决问题的角度来看）其表现也比使用长度惩罚时更差。使用固定的线性长度惩罚时，基因组长度在进化的早期阶段往往会急剧增加，然后下降到基于突变率的水平。平台期基因组长度（即最终种群中个体的平均长度）通常会随着碱基突变率的增加而增加。当VIV收敛时，个体内部往往会有许多良好替代基因的副本。在整个进化过程中，我们观察到许多活跃基因和非活跃基因之间切换的实例。这些观察结果支持了这样的结论，即非编码区充当了VIV可以探索替代基因值的暂存空间。这些结果代表了在理解遗传算法如何更好地利用生物进化的更多能力和灵活性，同时为理解不断进化的生物系统提供更好工具方面迈出的积极一步。
