# The Linkage Tree Genetic Algorithm

@inproceedings{Thierens2010TheLT, title={The Linkage Tree Genetic Algorithm}, author={Dirk Thierens}, booktitle={PPSN}, year={2010} }

We introduce the Linkage Tree Genetic Algorithm (LTGA), a competent genetic algorithm that learns the linkage between the problem variables. The LTGA builds each generation a linkage tree using a hierarchical clustering algorithm. To generate new offspring solutions, the LTGA selects two parent solutions and traverses the linkage tree starting from the root. At each branching point, the parent pair is recombined using a crossover mask defined by the clustering at that particular tree node. The…

## 82 Citations

Predetermined versus learned linkage models

- Computer ScienceGECCO '12
- 2012

Experimental results show that - for problems with intricate interaction structure - the a priori fixed models are actually less efficient than LTGA that dynamically learns a hierarchical tree model.

Linkage tree genetic algorithms: variants and analysis

- Computer ScienceGECCO '12
- 2012

The deceptive step trap problem is introduced, which shows the novel combination of smallest first subtrees ordering with global mixing is effective for black box optimization, while least linked first subtree ordering (LT-GOMEA) is effective on problems where partial reevaluation is possible.

Evolvability Analysis of the Linkage Tree Genetic Algorithm

- Computer SciencePPSN
- 2012

The linkage model evolVability measure and the evolvability-based fitness distance correlation prove to be useful tools to get an insight into the search properties of linkage model building genetic algorithms.

Linkage neighbors, optimal mixing and forced improvements in genetic algorithms

- Computer ScienceGECCO '12
- 2012

This paper considers a technique called Forced Improvements (FI), that allows LTGA to converge to a single solution without requiring an explicit, diversity-reducing, selection step, and considers a different linkage model, called Linkage Neighbors (LN), that is more flexible, yet can be learned equally efficiently from data.

Empirical performance evaluation of the linkage tree genetic algorithm

- Computer Science
- 2020

It is concluded that certain problem instances tested, Low Autocorrelation Binary Sequences, two instances of the Ising model, and the Maximum Independent Vertex Set benefit from linkage information as the LTGA performed very good compared to other algorithms on these.

Cooperative coevolutionary genetic algorithm using hierarchical clustering of linkage tree

- Computer ScienceGECCO Companion
- 2020

This paper proposes an algorithm that can efficiently search for problems with dependencies between variables by introducing Linkage Tree into CC methods and evaluated the search performance of the proposed method using benchmark functions and confirmed the performance improvement by comparing it with the conventional methods.

Hierarchical problem solving with the linkage tree genetic algorithm

- Computer ScienceGECCO '13
- 2013

Results show that, although LTGA is a simple algorithm compared to SEAM and hBOA, it nevertheless is a very efficient, reliable, and scalable algorithm for solving the randomly shuffled versions of HIFF and HTRAP, two hard, hierarchical problems.

On Measures to Build Linkage Trees in LTGA

- Computer SciencePPSN
- 2012

LTGA is a recent powerful linkage-learning EA that builds a hierarchical linkage model known as the linkage tree (LT).

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

- Computer ScienceNeurocomputing
- 2014

A Multifactorial Optimization Paradigm for Linkage Tree Genetic Algorithm

- Computer ScienceInf. Sci.
- 2020

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