Name
Abstract | ||
|---|---|---|
This work proposes a linkage-learning niching method that improves the capability of estimation of distribution algorithms (EDAs) on reducing spurious linkages which increase problems difficulty. Concatenated parity function (CPF), a class of allelic pairwise independent problems, causes exponential scalability for hierarchical Bayesian optimization algorithm (hBOA), which is one of powerful EDAs. Empirical results show that restricted tournament replacement (RTR) that hBOA employs results in spurious linkages and increases difficulty on solving CPF. Our research consists of these goals: (1) proposing a mutual information matrix to approximate the implicit linkage-information during EDAs' execution, (2) reducing spurious linkages by utilizing new metric of similarity, and (3) maintaining diversity of population. The results show that hBOA with our proposed niching method reduces the spurious linkages and solves CPF in the polynomial time. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2330784.2330903 | GECCO (Companion) |
Keywords | Field | DocType |
empirical result,linkage-learning niche,powerful edas,linkage-learning niching method,distribution algorithm,spurious linkage,allelic pairwise independent problem,proposed niching method,increases difficulty,increase problems difficulty,concatenated parity function,genetic algorithm,estimation of distribution algorithm,polynomial time,mutual information | EDAS,Population,Mathematical optimization,Estimation of distribution algorithm,Computer science,Pairwise independence,Artificial intelligence,Mutual information,Time complexity,Spurious relationship,Genetic algorithm,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsung-Yu Ho | 1 | 16 | 2.78 |
| Tian-Li Yu | 2 | 430 | 35.28 |