Abstract
In Chapter 7 we compared different GA approaches for handling constraints using an example of the transportation problem. It seems that for this particular class of problems we can do better: we can use a more appropriate (natural) data structure (for a transportation problem, a matrix) and specialized “genetic” operators which operate on matrices. Such an evolution program would be much stronger method than GENOCOP: the GENOCOP optimizes any function with linear constraints, whereas the new evolution program optimizes only transportation problems (these problems have precisely n + k − 1 equalities, where n and k denote the number of sources and destinations, respectively; see the description of the transportation problem below). However, it would be very interesting to see what can we gain by introducing extra problem-specific knowledge into an evolution program.
Necessity knows no law.
Publilius Syrus, Moral Sayings
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References
Portions reprinted, with permission, from IEEE Transactions on Systems, Man, and Cybernetics, Vol. 21, No. 2, pp. 445–452, 1991.
Portions reprinted, with permission, from ORSA Journal on Computing, Vol. 3, No. 4, 1991, pp. 307–316, 1991.
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© 1992 Springer-Verlag Berlin Heidelberg
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Michalewicz, Z. (1992). The Transportation Problem. In: Genetic Algorithms + Data Structures = Evolution Programs. Artificial Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-02830-8_10
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DOI: https://doi.org/10.1007/978-3-662-02830-8_10
Publisher Name: Springer, Berlin, Heidelberg
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