Multiscale island injection genetic algorithms for groundwater remediation

Sinha, Eva; Minsker, Barbara

doi:10.1016/j.advwatres.2007.03.006

Cited by 23 publications

(14 citation statements)

References 35 publications

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“…This removes non-ideal alphabets from the search and enables better alphabets to be refined after the discretization is changed to a finer level. These findings are consistent with those of Babbar and Minsker (2006) and Sinha and Minsker (2007) on a groundwater remediation application, but further testing is needed with other discretization approaches and applications.…”

Section: Discussionsupporting

confidence: 91%

“…For the first approach, multiscale island genetic algorithms have been proposed to mitigate the difference between faster, lower-resolution models (or low-cardinality representations of the fitness function) and more accurate models with higher computational cost (higher-cardinality representations.) Work done by Babbar (2002) and Sinha and Minsker (2007) suggest the efficacy of an island GA in which the fitness of population members is first evaluated at a coarse discretization, and then processed at a finer resolution once nominal convergence has been reached at the first resolution.…”

Section: Reduced Alphabetmentioning

confidence: 99%

“…Performance is improved by limiting fitness evaluations using the following algorithmic approaches: the micro-GA (Krishnakumar, 1989;Pico and Wainwright, 1994;Coello and Pulido, 2001), the probability-based compact GA (Mininno et al, 2008), memory enhancements specific to MPC (Onnen et al, 1997), and domainspecific methods that reduce the number of decision variables values. The latter approaches were inspired by multiscale GAs developed by Babbar and Minsker (2006) and Sinha and Minsker (2007) and noted by Maier et al (2014) as important to reducing the search space. All new GA approaches are compared to simple genetic algorithms with larger population sizes determined by GA theory (Reed et al, 2000;Minsker, 2005).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary algorithm enhancement for model predictive control and real-time decision support

Zimmer

Schmidt

Ostfeld

et al. 2015

Environmental Modelling & Software

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Section: Discussionsupporting

confidence: 91%

Section: Reduced Alphabetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary algorithm enhancement for model predictive control and real-time decision support

Zimmer

Schmidt

Ostfeld

et al. 2015

Environmental Modelling & Software

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“…Babbar and Minsker [20][21] in 2006, applied multi scale master slave parallel GAs for solving the groundwater remediation problems. Later Sinha and Minesker in 2007 [18], extended the work of Babbar and Minesker to more thoroughly consider a multiscale approach called Island Injection GA (IIGA). They showed that their study outperformed simple GAs in several studies.…”

Section: Applicationsmentioning

confidence: 97%

Applications of parallel genetic algorithms in ground water contamination problems: An overview

Sharma

2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

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Groundwater resource management is a challenging problem faced by almost all the countries. Mathematical models of these problems often turn out to be illdefined subject to several variables and constraints. Sophisticated algorithms are needed in order to deal efficiently with such problems. In the past few decades much attention has been paid to heuristic techniques like genetic algorithms etc which can easily solve such problems. Further, in order to tackle the large number of involved parameters in these problems parallel version of GAs is more effective than the basic GAs. In this paper an attempt is made to review the application of PGA on groundwater management problems.

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“…For problems that require evaluation of a computationally-intensive model with spatial dimensions, Babbar and Minsker (2006) and Sinha and Minsker (2007) developed multiscale GAs that reduce the search space by using coarse simulation model grids, periodically redirected using finer grids.…”

Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

532

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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Multiscale island injection genetic algorithms for groundwater remediation

Cited by 23 publications

References 35 publications

Evolutionary algorithm enhancement for model predictive control and real-time decision support

Evolutionary algorithm enhancement for model predictive control and real-time decision support

Applications of parallel genetic algorithms in ground water contamination problems: An overview

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

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