Learning building block structure from crossover failure
Zhenhua Li, Erik D. Goodman · 2007
In the classical binary genetic algorithm, although crossover within a building block (BB) does not always cause a decrease in fitness, any decrease in fitness results from the destruction of some building blocks, in problems where such structures are well defined, such as those considered here. Those crossovers that cause both offspring to be worse, or one to be worse and one unchanged, are here designated as failed crossovers. Counting the failure frequency of singlepoint crossovers performed at each locus reveals something of the BB structure. Guided by the failure record, GA operators could choose appropriate points for crossover, in order to work more efficiently and effectively. Experiments on test