Augmented evolutionary intelligence: combining human and evolutionary design for water distribution network optimisation
Augmented evolutionary intelligence: combining human and evolutionary design for water distribution network optimisation
复制标题
增强进化智能：结合人类和进化设计进行配水网络优化
DOI：
10.1145/3321707.3321814
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
D. Savić
中科院分区：
文献类型：
--
作者：
M. Johns;H. Mahmoud;D. Walker;N. D. Ross;E. Keedwell;D. Savić
进化算法（EA）已经被用于优化理论和现实世界的问题几十年。这些方法虽然能够产生接近最优的解决方案，但由于难以在目标函数中定义的考虑因素，通常无法满足现实世界的应用要求。一种解决方案是采用交互式进化算法（IEA），在优化过程中涉及专家人类实践者，以帮助指导算法更适合现实世界的实现。这种方法要求从业者在优化过程中做出数千个决策，可能导致用户疲劳并降低算法的搜索能力。这项工作提出了一种通过机器学习技术捕获工程专业知识，并通过其变异算子将由此产生的启发式集成到EA中的方法。基于人的启发式变异评估的一系列配水网络设计问题的文献，往往优于传统的EA方法。这些发展开辟了人类专家和进化技术之间更有效的相互作用的潜力，并有可能应用到水系统工程领域以外的更大和更多样化的问题。
Evolutionary Algorithms (EAs) have been employed for the optimisation of both theoretical and real-world problems for decades. These methods although capable of producing near-optimal solutions, often fail to meet real-world application requirements due to considerations which are hard to define in an objective function. One solution is to employ an Interactive Evolutionary Algorithm (IEA), involving an expert human practitioner in the optimisation process to help guide the algorithm to a solution more suited to real-world implementation. This approach requires the practitioner to make thousands of decisions during an optimisation, potentially leading to user fatigue and diminishing the algorithm's search ability. This work proposes a method for capturing engineering expertise through machine learning techniques and integrating the resultant heuristic into an EA through its mutation operator. The human-derived heuristic based mutation is assessed on a range of water distribution network design problems from the literature and shown to often outperform traditional EA approaches. These developments open up the potential for more effective interaction between human expert and evolutionary techniques and with potential application to a much larger and diverse set of problems beyond the field of water systems engineering.
水资源问题的进化算法算子与问题特征之间的接口分析。
影响因子：
4.9
作者：
McClymont K
通讯作者：
McClymont K
DOI：
--
发表时间：
2018
期刊：
--
影响因子：
--
作者：
Johns MB
通讯作者：
Johns MB
生成启发式方法来模仿配水网络优化中的专家
DOI：
--
发表时间：
2018
期刊：
--
影响因子：
--
作者：
Walker DJ
通讯作者：
Walker DJ