The Linkage Tree Genetic Algorithm

  title={The Linkage Tree Genetic Algorithm},
  author={Dirk Thierens},
  • 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… 
Predetermined versus learned linkage models
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
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
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
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
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
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
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
LTGA is a recent powerful linkage-learning EA that builds a hierarchical linkage model known as the linkage tree (LT).


CrossNet: a framework for crossover with network-based chromosomal representations
Two experiments support the hypothesis that CrossNet-based crossover can be useful, although performance improvements were modest, and conjecture that future work with the CrossNet framework will provide a useful new perspective for investigating linkage and chromosomal representations.
Network crossover performance on NK landscapes and deceptive problems
A method to build a network crossover operator that can be used in a GA to easily incorporate problem-specific knowledge and the performance of this operator is compared to other operators as well as the hierarchical Bayesian Optimization Algorithm on several different problem types.
Linkage in Evolutionary Computation
This work focuses on knowledge-based Evolutionary Linkage in MEMS Design Synthesis, which involves setting Representation and Multi-parent Learning within an Evolutionary Algorithm for Optimal Design of Trusses.
Performance of evolutionary algorithms on NK landscapes with nearest neighbor interactions and tunable overlap
This paper presents a class of NK landscapes with nearest-neighbor interactions and tunable overlap that is solvable in polynomial time using dynamic programming and related to scalability theory for genetic algorithms and estimation of distribution algorithms.
A new method for linkage learning in the ECGA
Experiments show that ClusterMI retains ECGA's scalability concerning population size while reducing the running time by $O(m^{0.7})$, resulting in speedups of potentially thousands of times.
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
This work discusses linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm and Hierarchical Bayesian Optimization Algorithm, as well as multiobjective Estimation of Distribution Algorithms.
Performance of Evolutionary Algorithms on Random Decomposable Problems
The paper tests the hierarchical Bayesian optimization algorithm (hBOA) and other evolutionary algorithms on a large number of random instances of the proposed class of problems and shows that hBOA can scalably solve rADPs and that it significantly outperforms all other methods included in the comparison.
Conquering hierarchical difficulty by explicit chunking: substructural chromosome compression
The proposed chromosome compression scheme is combined with the dependency structure matrix genetic algorithm and the restricted tournament replacement to create a scalable optimization tool which optimizes problems via hierarchical decomposition.
Hierarchical Clustering Using Mutual Information
We present a conceptually simple method for hierarchical clustering of data called mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits
Analyzing Deception in Trap Functions