论文标题
线性遗传编程的表型搜索轨迹网络
Phenotype Search Trajectory Networks for Linear Genetic Programming
论文作者
论文摘要
基因型到表型映射将基因型变化（例如突变）转化为表型变化。中立是某些突变不会导致表型变化的观察。研究基因型和表型空间中的搜索轨迹，尤其是通过中性突变，有助于我们更好地理解进化的进展及其算法行为。在这项研究中，我们将遗传编程系统的搜索轨迹可视化为基于图的模型，其中节点是基因型/表型和边缘代表其突变过渡。我们还定量测量了表型的特征，包括它们的基因型丰度（对中立的需求）和kolmogorov的复杂性。我们将这些量化的指标与搜索轨迹可视化联系起来，发现更复杂的表型的基因型较少所代表的代表性不足，并且很难进化发现。另一方面，不太复杂的表型被基因型过多的代表性，更容易找到，并且经常用作进化的垫脚石。
Genotype-to-phenotype mappings translate genotypic variations such as mutations into phenotypic changes. Neutrality is the observation that some mutations do not lead to phenotypic changes. Studying the search trajectories in genotypic and phenotypic spaces, especially through neutral mutations, helps us to better understand the progression of evolution and its algorithmic behaviour. In this study, we visualise the search trajectories of a genetic programming system as graph-based models, where nodes are genotypes/phenotypes and edges represent their mutational transitions. We also quantitatively measure the characteristics of phenotypes including their genotypic abundance (the requirement for neutrality) and Kolmogorov complexity. We connect these quantified metrics with search trajectory visualisations, and find that more complex phenotypes are under-represented by fewer genotypes and are harder for evolution to discover. Less complex phenotypes, on the other hand, are over-represented by genotypes, are easier to find, and frequently serve as stepping-stones for evolution.
