A study of the neutrality of Boolean function landscapes in genetic programming
The neutrality of genetic programming Boolean function landscapes is investigated in this paper. Compared with some well-known contributions on the same issue, (i) we first define new measures which help in characterizing neutral landscapes; (ii) we use a new sampling methodology, which captures features that are disregarded by uniform random sampling; (iii) we introduce new genetic operators to define the neighborhood of tree structures; and (iv) we compare the fitness landscape induced by different sets of functional operators. This study indicates the existence of a relationship between our neutrality measures and the performance of genetic programming for the problems studied. (C) 2011 Elsevier B.V. All rights reserved.
neutrality boolean functions genetic programming fitness landscapes problem difficulty negative slope coefficient