Continuous symbiotic program evolution

遗传编程（GP）是进化计算（EC）的一个分支，其总体目标是从候选程序的种群中进化出程序。目前，申请人的研究使来自同一试验的多个程序能够学习相互作用，以在进化过程中建立非重叠的合作行为。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（一个并行的部署框架）。这个项目进行了实质性的概括，其中程序进化是（1）一个连续的过程和（2）程序之间的交互可以是分层的，以及并行的。在（1）对持续进化过程的支持下，假设进化的竞争共同进化模型;因此，训练场景与学习者共同进化（例如，如在视频游戏中，难度随着玩家表现的提高而增加）。这也为发展不同的程序以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多样性维护的要求。在（2）中，将学习者限制在（程序的）并行部署形式中，如在合奏风格的“投票”框架中，自然会对可以发展解决方案的问题类型施加限制，即，你不能从以前学到的行为中建立新的行为。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，可以通过将简单的生物体纳入整体来构建更复杂的生物体。这个项目追求一个共生的框架下的层次模型建设GP。随着共生关系的增加，在早期训练场景中进化出的解决方案被完整地包含在内，并可能部署在与它们最初进化的环境不同的环境中。可扩展性仍然保持照顾竞争的共同进化;因此，随着学习者变得更有能力，训练场景被寻求促进从当前人群中推广多种行为，促进模型构建的连续过程。

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.