.png){kind=icon}
Return
Automatic generation of algorithms for robust optimisation problems using Grammar-Guided Genetic Programming
.png){kind=icon}
OA
AI
DOI：10.1016/j.cor.2021.105364.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We develop algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly the aim is to find a robust one, where the worst case in an uncertainty neighbourhood around a solution still performs well. To investigate improved methods we employ an automatic generation of algorithms approach: Grammar-Guided Genetic Programming. We develop algorithmic building blocks in a Particle Swarm Optimisation framework, define the rules for constructing heuristics from these components, and evolve populations of search algorithms for robust problems. Our algorithmic building blocks combine elements of existing techniques and new features, resulting in the investigation of a novel heuristic solution space. We obtain algorithms which improve upon the current state of the art. We also analyse the component level breakdowns of the populations of algorithms developed against their performance, to identify high-performing heuristic components for robust problems.
Keywords:
Robust optimisation
Implementation uncertainty
Metaheuristics
Global optimisation
Genetic programming
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.3
Papers:
6.5K
Citations:
1.8W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSaveEffects of two cooling garments on post-exercise thermal comfort of female subjects in the heat
PREAI
ShareSaveA largest empty hypersphere metaheuristic for robust optimisation with implementation uncertaintyOAAI
OAAIShareSave
View more