Multicore Parallel Genetic Algorithm with Tabu Strategy for Rainfall-Runoff Model Calibration

2017 ◽  
Vol 22 (8) ◽  
pp. 04017024 ◽  
Author(s):  
Shengli Liao ◽  
Qianying Sun ◽  
Chuntian Cheng ◽  
Ruhong Zhong ◽  
Huaying Su
Keyword(s):  
Genetic Algorithm ◽  
Model Calibration ◽  
Rainfall Runoff ◽  
Parallel Genetic Algorithm ◽  
Rainfall Runoff Model ◽  
Runoff Model

Data-Driven Bi-Objective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain

2016 ◽  
pp. 346-368 ◽  
Author(s):  
Nirupam Chakraborti
Keyword(s):  
Genetic Algorithm ◽  
Noisy Data ◽  
Prey Type ◽  
Data Driven ◽  
Neural Net ◽  
Predator Prey ◽  
Modeling And Optimization ◽  
Materials Research ◽  
Computational Materials ◽  
Data Driven Modeling

Data-driven modeling and optimization are now of utmost importance in computational materials research. This chapter presents the operational details of two recent algorithms EvoNN (Evolutionary Neural net) and BioGP (Bi-objective Genetic Programming) which are particularly suitable for modeling and optimization tasks pertinent to noisy data. In both the approaches a tradeoff between the accuracy and complexity of the candidate models are sought, ultimately leading to some optimum tradeoffs. These novel strategies are tailor-made for constructing models of right complexity, excluding the non-essential inputs. They are constructed to implement the notion of Pareto-optimality using a predator-prey type genetic algorithm, providing the user with a set of optimum models, out of which an appropriate one can be easily picked up by applying some external criteria, if necessary. Several materials related problems have been solved using these algorithms in recent times and a couple of typical examples are briefly presented in this chapter.

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