In this note, we briefly describe a new type of genetic algorithm that is designed to mitigate one or both of the following two major difficulties that traditional genetic algorithms may suffer: 1. When the number of "active genes" needs to be held constant or kept within some prescribed range, and 2. When the set of genes is much larger than the set of active genes of feasible solutions under consideration. These homogeneous genetic algorithms use (unordered) sets to represent "active genes"

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... chromosomes rather than strings, and accordingly the selection, mating and mutation operators are (naturally) defined using settheoretic operations. Homogeneous GAs have significantly outperformed traditional genetic algorithms for some typical problems in which these difficulties arise.