2 research outputs found
In Search of Optimal Linkage Trees
Linkage-learning Evolutionary Algorithms (EAs) use linkage
learning to construct a linkage model, which is exploited
to solve problems efficiently by taking into account important
linkages, i.e. dependencies between problem variables,
during variation. It has been shown that when this linkage
model is aligned correctly with the structure of the problem,
these EAs are capable of solving problems efficiently by
performing variation based on this linkage model [2]. The
Linkage Tree Genetic Algorithm (LTGA) uses a Linkage Tree
(LT) as a linkage model to identify the problem's structure
hierarchically, enabling it to solve various problems very
efficiently. Understanding the reasons for LTGA's excellent
performance is highly valuable as LTGA is also able to
efficiently solve problems for which a tree-like linkage model
seems inappropriate. This brings us to ask what in fact
makes a linkage model ideal for LTGA to be used