概括
本研究介绍了一种学习转移框架,通过优化任务之间的知识转移 (KT) 来增强进化的多任务处理 (EMT). 新方法提高了各种看不见的优化问题上的适应性和性能.
科学领域:
- 人工智能的人工智能
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 进化型多任务处理 (EMT) 使用知识转移 (KT) 来解决多任务优化问题 (MTOPs).
- 目前的隐性EMT方法由于操作员使用有限和对KT的状态利用不足,因此难以适应.
- 这导致不充分利用各种MTOP隐性KT的潜力.
研究的目的:
- 提出一种新的学习转移 (L2T) 框架,用于在EMT中自动发现高效的知识技术政策.
- 在EMT过程中将KT概念化为学习代理人的战略决策.
- 提高隐性EMT的适应性和性能,用于广泛的MTOP.
主要方法:
- 开发了一个L2T框架,其中包括动作/状态/奖励配方和交互环境.
- 雇佣了一个通过近接政策优化为学习代理进行培训的演员-关键网络.
- 整合了学习代理与各种进化算法来处理看不见的MTOP.
主要成果:
- 在L2T框架中,在适应性和性能方面取得了显著的改善.
- 对合成和现实世界MTOP的实证研究验证了框架的有效性.
- 这种方法成功地解决了各种各样的任务间关系,函数类和任务分配.
结论:
- 拟议的L2T框架通过实现有效的知识技术政策的自动发现来增强隐性EMT.
- 这种方法克服了现有方法的适应性和操作员利用的局限性.
- L2T框架为改善未见的MTOP上的进化算法的性能提供了一个有希望的方向.
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