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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03034naa a2200349 4500 | |
| 001 | oai:DiVA.org:mdh-34771 | |
| 003 | SwePub | |
| 008 | 170202s2016 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-347712 URI |
| 024 | 7 | a https://doi.org/10.1515/jaiscr-2016-00092 DOI |
| 040 | a (SwePub)mdh | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Leon, Miguelu Mälardalens högskola,Inbyggda system4 aut0 (Swepub:mdh)mlz02 |
| 245 | 1 0 | a Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters |
| 264 | c 2016-03-10 | |
| 264 | 1 | b Walter de Gruyter GmbH,c 2016 |
| 338 | a print2 rdacarrier | |
| 520 | a Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have been applied successfully to solve many real-world problems. However, the performance of DE is significantly influenced by its control parameters such as scaling factor and crossover probability. This paper proposes a new adaptive DE algorithm by greedy adjustment of the control parameters during the running of DE. The basic idea is to perform greedy search for better parameter assignments in successive learning periods in the whole evolutionary process. Within each learning period, the current parameter assignment and its neighboring assignments are tested (used) in a number of times to acquire a reliable assessment of their suitability in the stochastic environment with DE operations. Subsequently the current assignment is updated with the best candidate identified from the neighborhood and the search then moves on to the next learning period. This greedy parameter adjustment method has been incorporated into basic DE, leading to a new DE algorithm termed as Greedy Adaptive Differential Evolution (GADE). GADE has been tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtained the best rank among the counterparts in terms of the summation of relative errors across the benchmark functions with a high dimensionality. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskap0 (SwePub)1022 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciences0 (SwePub)1022 hsv//eng |
| 653 | a Differential evolution | |
| 653 | a Optimization | |
| 653 | a Parameter adaptation | |
| 700 | 1 | a Xiong, Ningu Mälardalens högskola,Inbyggda system4 aut0 (Swepub:mdh)nxg01 |
| 710 | 2 | a Mälardalens högskolab Inbyggda system4 org |
| 773 | 0 | t Journal of Artificial Intelligence and Soft Computing Researchd : Walter de Gruyter GmbHg 6:2, s. 103-118q 6:2<103-118x 2449-6499x 2083-2567 |
| 856 | 4 | u https://content.sciendo.com/downloadpdf/journals/jaiscr/6/2/article-p103.pdf |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-34771 |
| 856 | 4 8 | u https://doi.org/10.1515/jaiscr-2016-0009 |
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