MetadataShow full item record
In recent years new types of evolutionary algorithms have been introduced that try to improve traditional crossover operators by learning the structure of the problem, most notably the Linkage Tree Genetic Algorithm. For single-objective problems linkage learning has proven to be a valuable improvement over the traditional crossover operators, since it can automatically learn and exploit the structure of a speciﬁc problem. Some initial research has been done to extend the use of LTGA into the domain of multi-objective problems, but no research has been done that looks at the eﬀectiveness of linkage learning in a multi-objective environment. In order to do this some new multi-objective variants of the NK-landscape are introduced. For single-objective problems there are plenty of benchmark functions that model diﬀerent types of problem variable correlations, but adaptations of these benchmark functions for multi-objective environments often leave the variable correlations between objectives unchanged.