Irrigation planning using Genetic Algorithms

By K. Raju, D. Nagesh Kumar. 2004

115 citations recorded by the source corpus.

Subfields: Ocean Engineering, Soil Science, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/a74fb5cf58c7e6fbf545bf995b1cb84f10e201e1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ocean Engineering

1. Arunchandar Vasan
2. K. Raju

In Soil Science

1. Arunchandar Vasan
2. K. Raju

Citations in the corpus

• Irrigation scheduling of a classical gravity network based on the Covariance Matrix Adaptation - Evolutionary Strategy algorithm (2014, methodology): “Several other studies (Chen, 1997; Raju and Kumar, 2004; Mathur et al.; 2009; Elferchichia et al., 2009; Sharif and Wardlaw, 2000; Kuo et al., 2000) demonstrated the efficiency and the strength of genetic algorithm approach as an optimization tool to provide good solutions for an irrigation scheduli”
• Application of Differential Evolution for Irrigation Planning: An Indian Case Study (2007, background): “DE to convert the constrained problem (such as shown in LP formulation) into unconstrained problem (Deb 1995; Raju and Nagesh Kumar 2004 ).”