Linkage-learning Evolutionary Algorithms (EAs) use link- age 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 per- forming variation based on this linkage model . The Link- age 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.
|Title of host publication||Proceedings of Genetic and Evolutionary Computation Conference 2015|
|Publication status||Published - 1 Jan 2015|
|Event||GECCO 2015: Genetic and Evolutionary Computation Conference - Madrid, Spain|
Duration: 11 Jul 2015 → 15 Jul 2015
|Period||11/07/15 → 15/07/15|
|Other||A Recombination of the 24th International Conference on Genetic Algorithms (ICGA) and the 20nd Annual Genetic Programming Conference (GP).|