Continuous symbiotic program evolution

遗传规划（GP）是进化计算（EC）的一个子领域，其总体目标是从一群候选程序进化出程序。目前，申请人的研究使来自同一试验的多个程序能够学会相互作用，从而在进化过程中建立不重叠的合作行为。进化后，一些程序子集将学会在一个独特的环境子集下行动（并行部署框架）。这个项目进行了大量的推广，其中程序进化是(1)一个连续的过程，(2)程序之间的交互可以是分层的，也可以是并行的。在(1)支持连续进化过程的假设下，进化的竞争共同进化模式；因此，训练场景是与学习者共同进化的（例如，在电子游戏中，难度随着玩家表现的提高而增加）。这也为发展不同的程序来解决不同的任务子集提供了基础，但在学习者群体和代表训练场景的群体中引入了持续保持多样性的需求。在(2)中，将学习器限制为并行部署形式（程序），如在集成风格的“投票”框架中，自然会对解决方案可以进化的问题类型施加约束，即，您不能从先前学习的行为中构建新的行为。相反，在生物学背景下，共生被广泛认为是一种非常重要的机制，通过将更简单的生物纳入其整体来构建更复杂的生物。本项目追求在GP下构建层次模型的共生框架。随着额外的共生层的增加，在早期训练场景中进化的解决方案被包含在它们的整体中，并可能部署在与它们最初进化的环境不同的环境中。可扩展性仍然关注竞争性协同进化；因此，随着学习者的能力越来越强，需要寻找训练场景，以促进从当前人群中归纳出多种行为，从而促进持续的模型构建过程。

Genetic Programming (GP) is a subfield of Evolutionary Computation (EC) in which the general goal is to evolve programs from a population of candidate programs. Currently the applicant's research enables multiple programs from the same trial to learn to interact to establish a non-overlapping cooperative behavior during evolution. Post evolution, some subset of programs would have learnt to act under a unique subset of circumstances (a parallel framework of deployment). This project undertakes a substantial generalization in which program evolution is (1) a continuous process and (2) the interaction between programs can be hierarchical as well as parallel. Under (1) support for a continuous process of evolution assumes a competitive coevolutionary model of evolution; thus, training scenarios are coevolved with the learners (e.g., as in video game difficulty increasing as player performance improves). This also provides the basis for evolving different programs to solve different subsets of tasks, but introduces the requirement for continuous diversity maintenance in both the learner population and the population representing training scenarios. Under (2), limiting learners to a parallel form of deployment (of programs), as in ensemble style `voting' frameworks, will naturally place constraints on the types of problems against which solutions can be evolved i.e., you cannot build a new behavior from one previously learnt. Conversely, under a biological context, symbiosis has been widely acknowledged as a very important mechanism for building more complex organisms by subsuming simpler organisms in their entirety. This project pursues a symbiotic framework for hierarchical model building under GP. As additional layers of symbiosis are added, solutions evolved in earlier training scenarios are subsumed in their entirety and potentially deployed in contexts dissimilar from that in which they were originally evolved. Scalability still holds care of competitive coevolution; thus, as learners become more capable, training scenarios are sought which promote the generalization of multiple behaviours from the current population, promoting a continuous process of model building.