Theory and Algorithms Commons^™

Functional optimization is profoundly affected by the use of specific encodings. In one encoding, a particular problem may be simple to undertake, while in another encoding, the problem may be intractible. Genetic algorithms solve optimization problems by making use of schema. By locating schema in a solution vector, the paradigm can settle on a solution that makes use of several schema and combines them via crossover.

We propose a generalization of this idea, conjugate schema. Conjugate schema are disjoint subsets of the basis over which the fitness function can be written as a sum of smaller dimensional functions. We find …