Enhancements to Genetic Algorithm for Optimal Ground-Water Management

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Abstract
Genetic algorithm (GA) is considered to be a robust technique for solving ground-water optimization problems. Often these problems are difficult to solve using traditional gradient-based techniques as these are nonlinear, nonconvex, and discontinuous. In this manuscript, recent research related to application of GA in solving these problems is critically reviewed, and three areas of potential enhancement to GA are identified and explored. These enhancement methods to GA are fitness reduction method (FRM), search bound sampling method (SBSM), and optimal resource allocation guideline (ORAG). In order to assess these methods, a nonlinear ground-water problem with fixed and variable costs is selected (from literature) where the corresponding optimal solution using a gradient-based nonlinear programming (NLP) technique is available. The problem is resolved using GA coupled with the enhancement methods, and the GA solutions are compared with the NLP solution. In addition, the sensitivity of these methods to va...