GA for deceptive problems: inverting schemata by a statistical approach
Adriana Agapie, Adrian-Horia Dediu · 2002
We propose a method to overcome the premature stagnation of genetic algorithms (GA). We first define an additional population in order to store a larger part of the resulting chromosomes. We then extract the schema responsible for the stagnation of the algorithm, derive its complementary schema and resume the GA's evolution with some fixed positions in the chromosome. We prove that, if working with a directed mutation, the GA will explore better than if it carried on with the canonical genetic operators.