Applying Genetic Algorithms to Real-World Problems
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This paper outlines what the author perceives as crucial ingredients of a successful application of Genetic Algorithms (GAs) to real-world combinatorial problems. First, the importance of the Schema Theorem is stressed, pointing to crossover as the most potent force in a GA. Second, the importance of an encoding and operators adapted to the problem being solved is demonstrated, with two implications: the importance of the binary alphabet has been largely overstated in the past (in many problems it is not only unwarranted, it is detrimental), and practical GAS must be built to solve problems (i.e., sets of instances) rather than (arbitrary) functions.
The benefits of the above guidelines are illustrated by the Grouping GA (GGA), applied to three different grouping problems, namely Bin Packing and its variety Line Balancing, Equal Piles and Economies of Scale. The first application suggests a superiority of crossover-based search over a classic Branch & Bound, the second shows the superiority of the GGA over standard GAs applied to grouping problems, and the third illustrates the kind of industrial applications GAS can be called upon to solve.
KeywordsGenetic Algorithm Tabu Search Crossover Operator Line Balance Standard Crossover
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