为结构工程设计开发新型基因监管网络
Rahul Dubey1,2, Simon Hickinbotham3, Andrew Colligan4
1Missouri State University, Department of Computer Science.
Artificial life
|August 16, 2024
概括
本研究介绍了一种进化和发展 (evo-devo) 框架,用于自动化结构工程设计. 新的真实编码NEAT算法有效地演变多样化的设计,并优于现有的方法.
科学领域:
- 工程 工程师 工程师 工程师
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 工程设计优化通常需要人类的专业知识,并与一般化性作斗争.
- 现有的设计生成搜索技术在参数调整和可扩展性方面存在局限性.
- 进化和发展 (evo-devo) 概念为自动化复杂的设计过程提供了一种生物启发的方法.
研究的目的:
- 通过使用evo-devo原则引入一个用于自动化结构工程设计演变的新框架.
- 探索通过神经网络和遗传编程实现的人工基因调节网络 (GRNs).
- 开发和评估一种新的神经进化方法,即实时编码的NEAT (RNEAT),用于进化各种工程设计.
主要方法:
- 由进化和发育 (evo-devo) 生物学所启发的框架被开发出来.
- 人工基因调节网络 (GRNs) 通过神经网络和遗传编程来模拟.
- 一种新的实值编码的神经进化方法,实值编码NEAT (RNEAT),被引入进化人工GRNs.
主要成果:
- 拟议的框架成功地为2D和3D问题生成了多样化的结构设计.
- 实时编码的NEAT (RNEAT) 算法在超过50%的测试问题中表现出与已建立的进化技术相比更高的性能.
- 该研究验证了使用evo-devo原则用于自动化工程设计进化的概念.
结论:
- 灵感来自evo-devo的框架为自动化结构工程设计的演变提供了一种有效的方法.
- 在RNEAT算法显示显著的希望产生多样化和优化的工程解决方案.
- 这项研究验证了生物灵感计算方法在推进工程设计优化方面的潜力.
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