posted on 2013-08-08, 10:53authored byEdgar Galván-López, Brendan Cody-Kenny, Leonardo Trujillo, Ahmed Kattan
Research on semantics in Genetic Programming
(GP) has increased over the last number of years. Results in this
area clearly indicate that its use in GP considerably increases
performance. Many of these semantic-based approaches rely on
a trial-and-error method that attempts to find offspring that
are semantically different from their parents over a number
of trials using the crossover operator (crossover-semantics
based - CSB). This, in consequence, has a major drawback:
these methods could evaluate thousands of nodes, resulting in
paying a high computational cost, while attempting to improve
performance by promoting semantic diversity. In this work,
we propose a simple and computationally inexpensive method,
named semantics in selection, that eliminates the computational
cost observed in CSB approaches. We tested this approach
in 14 GP problems, including continuous- and discrete-valued
fitness functions, and compared it against a traditional GP and
a CSB approach. Our results are equivalent, and in some cases,
superior than those found by the CSB approach, without the
necessity of using a “brute force” mechanism.
Funding
U.S.-Hungary Cooperative Mathematical Research on Vilenkin- Fourier Series