Illinois Genetic Algorithms Laboratory

Van Hoyweghen, C., Goldberg, D.E., Naudts, B. (2001)
TR No.: 2001030 | Download PDF | Download PS

Abstract:
The paper shows that there is a key dividing line between two types of
symmetrical problems: problems for which a genetic algorithm (GA) benefits from the fact that
genetic drift chooses between equally good partial solutions, and problems for
which all equally good partial solutions have to be preserved to find an
optimum. By analyzing in detail the search process of a selectorecombinative GA optimizing
a TwoMax and comparing this search process with that of an Ising model, the paper
investigates the difference between these two types of symmetrical problems.
For the first type of problems, naively adding a niching technique to the
genetic algorithm makes the problem harder to solve. For the last type of problems,
niching is necessary to find an optimum.