Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
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The building block hypothesis implies that the epistatic property of a given problem must be connected well to the linkage property of the employed representation and crossover operator in the design of genetic algorithms. A good handling of building blocks has much to do with topological linkages of genes in the chromosome. In this paper, we provide a taxonomy of the approaches that exploit topological linkages of genes. They are classified into three models: static linkage model, adaptive linkage model, and evolvable linkage model. We also provide an overview on the chromosomal structures, encodings, and operators supporting each of the models.