Direct linkage discovery with empirical linkage learning
| Autor: | Michal W. Przewozniczek, Marcin M. Komarnicki, Bartosz Frej |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject Evolutionary algorithm 0102 computer and information sciences 02 engineering and technology Linkage (mechanical) Machine learning computer.software_genre 01 natural sciences law.invention 010201 computation theory & mathematics law 0202 electrical engineering electronic engineering information engineering Decomposition (computer science) 020201 artificial intelligence & image processing Quality (business) Artificial intelligence business computer Model building Linkage learning media_common |
| Zdroj: | GECCO |
| DOI: | 10.1145/3449639.3459333 |
| Popis: | Problem decomposition is an important part of many state-of-the-art Evolutionary Algorithms (EAs). The quality of the decomposition may be decisive for the EA effectiveness and efficiency. Therefore, in this paper, we focus on the recent proposition of Linkage Learning based on Local Optimization (3LO). 3LO is an empirical linkage learning (ELL) technique and is proven never to report the false linkage. False linkage is one of the possible linkage defects and occurs when linkage marks two independent genes as a dependent. Although thanks to the problem decomposition quality, the use of 3LO may lead to excellent results, its main disadvantage is its high computational cost. This disadvantage makes 3LO not applicable to state-of-the-art EAs that originally employed Statistical-based Linkage Learning (SLL) and frequently update the linkage information. Therefore, we propose the Direct Linkage Empirical Discovery technique (DLED) that preserves 3LO advantages, reduces its costs, and we prove that it is precise in recognizing the direct linkage. The concept of direct linkage, which we identify in this paper, is related to the quality of the decomposition of overlapping problems. The results show that incorporating DLED in three significantly different state-of-the-art EAs may lead to promising results. |
| Databáze: | OpenAIRE |
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