Knowledge-guided Genetic Algorithm for input parameter optimisation in environmental modelling

Wendt, Kerstin; Cortés, Ana; Margalef, Tomás

doi:10.1016/j.procs.2010.04.152

Cited by 18 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GAs have the following advantages: (1) GAs are a multiple-point-based search approach, (2) the search scheme of GAs is directly guided by the objective function, and (3) GAs employ probabilistic transition rules to avoid local optima. Because of these advantages, GAs are recognized as a robust method for complex problems (Wendt et al, 2010;Wang et al, 2012b).…”

Section: Genetic Algorithm and Its Parallelization Using The Hpc Job mentioning

confidence: 99%

Double-layer parallelization for hydrological model calibration on HPC systems

Zhang

et al. 2016

Journal of Hydrology

View full text Add to dashboard Cite

s u m m a r yLarge-scale problems that demand high precision have remarkably increased the computational time of numerical simulation models. Therefore, the parallelization of models has been widely implemented in recent years. However, computing time remains a major challenge when a large model is calibrated using optimization techniques. To overcome this difficulty, we proposed a double-layer parallel system for hydrological model calibration using high-performance computing (HPC) systems. The lower-layer parallelism is achieved using a hydrological model, the Digital Yellow River Integrated Model, which was parallelized by decomposing river basins. The upper-layer parallelism is achieved by simultaneous hydrological simulations with different parameter combinations in the same generation of the genetic algorithm and is implemented using the job scheduling functions of an HPC system. The proposed system was applied to the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River, to calibrate the model effectively by making full use of the computing resources in the HPC system and to investigate the model's behavior under various parameter combinations. This approach is applicable to most of the existing hydrology models for many applications.

show abstract

Section: Genetic Algorithm and Its Parallelization Using The Hpc Job mentioning

confidence: 99%

Double-layer parallelization for hydrological model calibration on HPC systems

Zhang

et al. 2016

Journal of Hydrology

View full text Add to dashboard Cite

show abstract

“…This is the approach that we adopted in our implementation. As an alternative, initial parameters can also be obtained from well documented research and from domain experts [78]. Such an approach has the advantage of providing an anchor between existing knowledge and new research.…”

Section: Discussionmentioning

confidence: 99%

An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

Oloo

Wallentin

2017

IJGI

View full text Add to dashboard Cite

Conventionally, agent-based modelling approaches start from a conceptual model capturing the theoretical understanding of the systems of interest. Simulation outcomes are then used "at the end" to validate the conceptual understanding. In today's data rich era, there are suggestions that models should be data-driven. Data-driven workflows are common in mathematical models. However, their application to agent-based models is still in its infancy. Integration of real-time sensor data into modelling workflows opens up the possibility of comparing simulations against real data during the model run. Calibration and validation procedures thus become automated processes that are iteratively executed during the simulation. We hypothesize that incorporation of real-time sensor data into agent-based models improves the predictive ability of such models. In particular, that such integration results in increasingly well calibrated model parameters and rule sets. In this contribution, we explore this question by implementing a flocking model that evolves in real-time. Specifically, we use genetic algorithms approach to simulate representative parameters to describe flight routes of homing pigeons. The navigation parameters of pigeons are simulated and dynamically evaluated against emulated GPS sensor data streams and optimised based on the fitness of candidate parameters. As a result, the model was able to accurately simulate the relative-turn angles and step-distance of homing pigeons. Further, the optimised parameters could replicate loops, which are common patterns in flight tracks of homing pigeons. Finally, the use of genetic algorithms in this study allowed for a simultaneous data-driven optimization and sensitivity analysis.

show abstract

“…Interactive evolutionary systems (Eiben and Smith, 2015 ) use expert guidance to emulate a holistic fitness function that would otherwise depend on a very restricted pre-defined fitness model. The potential richness of such knowledge can be leveraged in not just evolutionary parent selection but can also optimize other parameters that leads to faster convergence, especially in mutations (Wendt et al, 2010 ). ILP has been shown to be conceptually similar to mutative EA in the context of program induction (Wong and Leung, 1997 ) and hence knowledge-guided mutations are related to knowledge augmented search in ILP.…”

Section: Background and Related Workmentioning

confidence: 99%

Few-Shot Induction of Generalized Logical Concepts via Human Guidance

et al. 2020

View full text Add to dashboard Cite

We consider the problem of learning generalized first-order representations of concepts from a small number of examples. We augment an inductive logic programming learner with 2 novel contributions. First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization. Second, we leverage richer human inputs in the form of advice to improve the sample efficiency of learning. We prove that the proposed distance measure is semantically valid and use that to derive a PAC bound. Our experiments on diverse learning tasks demonstrate both the effectiveness and efficiency of our approach.

show abstract

Knowledge-guided Genetic Algorithm for input parameter optimisation in environmental modelling

Cited by 18 publications

References 9 publications

Double-layer parallelization for hydrological model calibration on HPC systems

Double-layer parallelization for hydrological model calibration on HPC systems

An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

Few-Shot Induction of Generalized Logical Concepts via Human Guidance

Contact Info

Product

Resources

About