  Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm
(EA) to solve complex problems using the linkage information between problem
variables. LTGA performs well in various kinds of single-task optimization and
yields promising results in comparison with the canonical genetic algorithm.
However, LTGA is an unsuitable method for dealing with multi-task optimization
problems. On the other hand, Multifactorial Optimization (MFO) can
simultaneously solve independent optimization problems, which are encoded in a
unified representation to take advantage of the process of knowledge transfer.
In this paper, we introduce Multifactorial Linkage Tree Genetic Algorithm
(MF-LTGA) by combining the main features of both LTGA and MFO. MF-LTGA is able
to tackle multiple optimization tasks at the same time, each task learns the
dependency between problem variables from the shared representation. This
knowledge serves to determine the high-quality partial solutions for supporting
other tasks in exploring the search space. Moreover, MF-LTGA speeds up
convergence because of knowledge transfer of relevant problems. We demonstrate
the effectiveness of the proposed algorithm on two benchmark problems:
Clustered Shortest-Path Tree Problem and Deceptive Trap Function. In comparison
to LTGA and existing methods, MF-LTGA outperforms in quality of the solution or
in computation time.
