転移学習付き遺伝的ネットワークプログラミングとその応用に関する研究

该项目的主要目标是将迁移学习（TL）集成到进化计算（EC）算法中——遗传网络规划（GNP）——以开发先进的优化技术，该技术能够显着提高传统EC算法的进化效率（解决问题）。针对上述目标，实现了以下课题：开发了先进的GNP，特别是基于GNP的数据挖掘模型，实现了EC.2迁移学习的基本框架。2 .将基于强化学习（RL）的方案引入到GNP中，提出了一种新的具有知识转移的GNP算法（GNP- kt），该算法能够在不同的机器人情境中进行知识转移。3 .将基于强化学习的方案集成到学习分类器系统中，实现了基于规则的知识转移。基本上，可转移电子商务指出了电子商务的一个新的研究方向，这将在未来得到进一步的推广。

The primary goal of the project is to integrate transfer learning (TL) into an evolutionary computation (EC) algorithm -- genetic network programming (GNP) -- to develop advanced optimization techniques, which is capable of significantly improving the evolution efficiency (problem solving) of traditional EC algorithms. With respect to the above target, the following topics have been realized:1. Developed advanced GNP, especially GNP-based data mining model, to realize the fundamental framework of transfer learning in EC.2. Developed reinforcement learning (RL)-based scheme into GNP to develop a new algorithm GNP with knowledge transfer (GNP-KT), which can transfer knowledge across different robot situations.3. Integrated the RL-based scheme into the learning classifier systems (LCSs), which realize the rule-based knowledge transfer.4. Basically, the transferable EC pointed out a new research direction of EC, which will be further promoted in the future.