The Linkage Tree Genetic Algorithm

@inproceedings{Thierens2010TheLT,
  title={The Linkage Tree Genetic Algorithm},
  author={Dirk Thierens},
  booktitle={PPSN},
  year={2010}
}
  • D. Thierens
  • Published in PPSN 11 September 2010
  • Computer Science
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… 
Learning the Neighborhood with the Linkage Tree Genetic Algorithm
TLDR
The Linkage Tree Genetic Algorithm (LTGA), a population-based, stochastic local search algorithm that learns the neighborhood by identifying the problem variables that have a high mutual information in a population of good solutions, is considered.
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TLDR
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.
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TLDR
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TLDR
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.
Parallelizing the Linkage Tree Genetic Algorithm and Searching for the Optimal Replacement for the Linkage Tree
TLDR
Two parallel implementations of LTGA are presented that enable us to leverage the computational power of a multi-processor architecture and solve a problem that previously could not be solved, being the problem of finding high-quality predetermined linkage models that result in a better performance ofLTGA for intricate problems by replacing the online-learned LTs.
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TLDR
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
TLDR
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.
On the usefulness of linkage processing for solving MAX-SAT
TLDR
This paper focuses on exploring different methods of building family of subsets (FOS) linkage models, which are then used with the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) to solve MAX-SAT problems and shows that combining LS with LTGA or SAT-GomeA increases their performance.
Cooperative coevolutionary genetic algorithm using hierarchical clustering of linkage tree
TLDR
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
TLDR
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.
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