At the fundamental conceptual level, two alternatives have traditionally been considered for how mutations arise and how evolution happens: 1) random mutation and natural selection, and 2) Lamarckism. Recently, the theory of Interaction-based Evolution (IBE) has been proposed, according to which mutations are neither random nor Lamarckian, but are influenced by information accumulating internally in the genome over generations. Based on the estimation-of-distribution algorithms framework, we present a simulation model that demonstrates nonrandom, non-Lamarckian mutation concretely while capturing indirectly several aspects of IBE: selection, recombination, and nonrandom, non-Lamarckian mutation interact in a complementary fashion; evolution is driven by the interaction of parsimony and fit; and random bits do not directly encode improvement but enable generalization by the manner in which they connect with the rest of the evolutionary process. Connections are drawn to Darwin's observations that changed conditions increase the rate of production of heritable variation; to the causes of bell-shaped distributions of traits and how these distributions respond to selection; and to computational learning theory, where analogizing evolution to learning in accord with IBE casts individuals as examples and places the learned hypothesis at the population level. The model highlights the importance of incorporating internal integration of information through heritable change in both evolutionary theory and evolutionary computation.

翻译：在基本概念层面，传统上对于突变如何产生及进化如何发生存在两种备选解释：1）随机突变与自然选择，2）拉马克主义。近期提出的交互驱动进化理论（IBE）认为突变既非随机也非拉马克式，而是受基因组内代际累积信息的影响。本研究基于分布估计算法框架，构建了一个仿真模型，具体呈现了非随机、非拉马克式突变，同时间接捕捉IBE的多个方面：选择、重组与非随机非拉马克突变以互补方式交互作用；进化由简约性与适应性的相互作用驱动；随机比特并非直接编码改进，而是通过其与进化过程中其他环节的联结方式实现泛化。本研究与以下领域建立关联：达尔文关于环境变化增加可遗传变异产生速率的观察；性状钟形分布的成因及其对选择的响应；以及计算学习理论——类比将进化视为符合IBE的学习过程，将个体视为样例，并将习得假设置于群体层面。该模型凸显了在进化理论与进化计算中通过可遗传变化实现信息内部整合的重要性。