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Genetic Algorithms and Their Applications

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Springer Handbook of Engineering Statistics

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Abstract

The first part of this chapter describes the foundation of genetic algorithms. It includes hybrid genetic algorithms, adaptive genetic algorithms, and fuzzy logic controllers. After a short introduction to genetic algorithms, the second part describes combinatorial optimization problems including the knapsack problem, the minimum spanning tree problem, the set-covering problem, the bin-packing problem, and the traveling-salesman problem; these are combinatorial optimization problems which are characterized by a finite number of feasible solutions. The third part describes network design problems. Network design and routing are important issues in the building and expansion of computer networks. In this part, the shortest-path problem, maximum-flow problem, minimum-cost-flow problem, centralized network design, and multistage process planning problem are introduced. These problems are typical network problems and have been studied for a long time. The fourth section describes scheduling problems. Many scheduling problems from manufacturing industries are quite complex in nature and very difficult to solve by conventional optimization techniques. In this part the flow-shop sequencing problem, job-shop scheduling, the resource-constrained project scheduling problem, and multiprocessor scheduling are introduced. The fifth part introduces the reliability design problem, including simple genetic algorithms for reliability optimization, reliability design with redundant units and alternatives, network reliability design, and tree-based network topology design. The sixth part describes logistic problems including the linear transportation problem, the multiobjective transportation problem, the bicriteria transportation problem with fuzzy coefficients, and supply chain management network design. Finally, the last part describes location and allocation problems including the location-allocation problem, capacitated plant-location problem, and obstacle location-allocation problem.

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Acknowledgements

This work is supported by the Grant-in-Aid for Scientific Research (C) of the Japan Society for the Promotion of Science (JSPS: 19K12148).

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Authors and Affiliations

  1. Fuzzy Logic Systems Institute (FLSI), Department of Research, Fukuoka, Japan

    Mitsuo Gen

  2. Tokyo University of Science (TUS), Research Institute of Science & Technology, Tokyo, Japan

    Mitsuo Gen

  3. Dalian University of Technology, Economy and Technology Development Area, Dalian, China

    Lin Lin

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  1. Mitsuo Gen
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Correspondence to Mitsuo Gen .

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  1. Department of Industrial & Systems Engineering, Rutgers University, Piscataway, NJ, USA

    Hoang Pham

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    Gen, M., Lin, L. (2023). Genetic Algorithms and Their Applications. In: Pham, H. (eds) Springer Handbook of Engineering Statistics. Springer Handbooks. Springer, London. https://doi.org/10.1007/978-1-4471-7503-2_33

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