A Sensitivity Analysis of Critical Genetic Algorithm Parameters: Highway Alignment Optimization Case Study

A Sensitivity Analysis of Critical Genetic Algorithm Parameters: Highway Alignment Optimization Case Study

Eungcheol Kim (Incheon National University, South Korea), Manoj K. Jha (Morgan State University, USA) and Min-Wook Kang (University of South Alabama, USA)
DOI: 10.4018/978-1-4666-8473-7.ch054
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Genetic Algorithms (GAs) have been applied in many complex combinatorial optimization problems and have been proven to yield reasonably good solutions due to their ability of searching in continuous spaces and avoiding local optima. However, one issue in GA application that needs to be carefully explored is to examine sensitivity of critical parameters that may affect the quality of solutions. The key critical GA parameters affecting solution quality include the number of genetic operators, the number of encoded decision variables, the parameter for selective pressure, and the parameter for non-uniform mutation. The effect of these parameters on solution quality is particularly significant for complex problems of combinatorial nature. In this paper the authors test the sensitivity of critical GA parameters in optimizing 3-dimensional highway alignments which has been proven to be a complex combinatorial optimization problem for which an exact solution is not possible warranting the application of heuristics procedures, such as GAs. If GAs are applied properly, similar optimal solutions should be expected at each replication. The authors perform several example studies in order to arrive at a general set of conclusions regarding the sensitivity of critical GA parameters on solution quality. The first study shows that the optimal solutions obtained for a range of scenarios consisting of different combinations of the critical parameters are quite close. The second study shows that different optimal solutions are obtained when the number of encoded decision variables is changed.
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Optimizing highway alignments (Jong & Schonfeld 2003; Jha, Schonfeld, Jong, & Kim, 2006) involves selection of the best alignment among many alternatives based on specified objectives while satisfying various design and operational constraints. Since there are numerous factors (such as topography, right-of-way, environmental and socio-economic characteristics, to name a few) that influence location of highway alignments, a comprehensive formulation of the objective function and selection of a good search algorithm is highly desirable. Our previous efforts (see Table 1) have been largely devoted to:

  • 1.

    Objective function development;

  • 2.

    Genetic algorithm development for search and optimization; and

  • 3.

    Studying problems with different characteristics that represent real-world scenarios.

Recently, we have also investigated bi-level and multi-objective approaches to the highway alignment problem (Kang, Yang, Schonfeld, & Jha, 2010; Maji & Jha 2009). Many details of the highway alignment optimization problem, such as impact of the topography on alignment selection has been skipped here since those can be found in previously published works. A brief reference of previously published works by our research team in the field of highway alignment optimization is shown in Table 1.

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