Linearized domain adaptation in evolutionary multitasking
| Autor: | Tan Puay Siew, Yew-Soon Ong, Kavitesh Kumar Bali, Abhishek Gupta, Liang Feng |
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| Rok vydání: | 2017 |
| Předmět: |
Similarity (geometry)
Theoretical computer science Computer science business.industry Crossover Evolutionary algorithm 02 engineering and technology Evolutionary computation Task (project management) 020204 information systems 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Human multitasking 020201 artificial intelligence & image processing Artificial intelligence business Knowledge transfer |
| Zdroj: | CEC |
| DOI: | 10.1109/cec.2017.7969454 |
| Popis: | Recent analytical studies have revealed that in spite of promising success in problem solving, the performance of evolutionary multitasking deteriorates with decreasing similarity between constitutive tasks. The present day multifactorial evolutionary algorithm (MFEA) is susceptible to negative knowledge transfer between uncorrelated tasks. To alleviate this issue, we propose a linearized domain adaptation (LDA) strategy that transforms the search space of a simple task to the search space similar to its constitutive complex task. This high order representative space resembles high correlation with its constitutive task and provides a platform for efficient knowledge transfer via crossover. The proposed framework, LDA-MFEA is tested on several benchmark problems constituting of tasks with different degrees of similarities and intersecting global optima. Experimental results demonstrate competitive performances against MFEA and shows that our proposition dramatically improves the performance relative to optimizing each task independently. |
| Databáze: | OpenAIRE |
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