The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
불규칙하고 복잡한 다중 모드 환경의 최적화 문제의 경우 EDA(분포 알고리즘 추정)는 다른 진화 알고리즘과 유사한 조기 수렴이라는 단점을 겪습니다. 본 논문에서는 AP(Affinity Propagation) 클러스터링 분석을 기반으로 하는 적응형 틈새 EDA를 제안합니다. AP 클러스터링은 틈새를 적응적으로 분할하고 진화 프로세스에서 검색 정보를 마이닝하는 데 사용됩니다. 획득된 정보는 밸런스 니칭 검색 전략을 사용하여 EDA 성능을 향상시키는 데 성공적으로 활용됩니다. 두 가지 서로 다른 범주의 최적화 문제가 제안된 적응형 틈새 EDA를 평가하는 데 사용됩니다. 첫 번째는 단일 가우스 확률 모델을 기반으로 하는 연속 EDA를 통해 세 가지 벤치마크 기능 다중 모드 최적화 문제를 해결하는 것입니다. 다른 하나는 실제로 복잡한 이산 EDA 최적화 문제인 HP 모델 단백질 접힘을 해결하는 것입니다. k-Markov 확률 모델을 주문합니다. 시뮬레이션 결과는 제안된 적응형 틈새 EDA가 효율적인 방법임을 보여줍니다.
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부
Benhui CHEN, Jinglu HU, "An Adaptive Niching EDA with Balance Searching Based on Clustering Analysis" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 10, pp. 1792-1799, October 2010, doi: 10.1587/transfun.E93.A.1792.
Abstract: For optimization problems with irregular and complex multimodal landscapes, Estimation of Distribution Algorithms (EDAs) suffer from the drawback of premature convergence similar to other evolutionary algorithms. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine the searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by using a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first one is solving three benchmark functional multimodal optimization problems by a continuous EDA based on single Gaussian probabilistic model; the other one is solving a real complicated discrete EDA optimization problem, the HP model protein folding based on k-order Markov probabilistic model. Simulation results show that the proposed adaptive niching EDA is an efficient method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.1792/_p
부
@ARTICLE{e93-a_10_1792,
author={Benhui CHEN, Jinglu HU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Adaptive Niching EDA with Balance Searching Based on Clustering Analysis},
year={2010},
volume={E93-A},
number={10},
pages={1792-1799},
abstract={For optimization problems with irregular and complex multimodal landscapes, Estimation of Distribution Algorithms (EDAs) suffer from the drawback of premature convergence similar to other evolutionary algorithms. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine the searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by using a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first one is solving three benchmark functional multimodal optimization problems by a continuous EDA based on single Gaussian probabilistic model; the other one is solving a real complicated discrete EDA optimization problem, the HP model protein folding based on k-order Markov probabilistic model. Simulation results show that the proposed adaptive niching EDA is an efficient method.},
keywords={},
doi={10.1587/transfun.E93.A.1792},
ISSN={1745-1337},
month={October},}
부
TY - JOUR
TI - An Adaptive Niching EDA with Balance Searching Based on Clustering Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1792
EP - 1799
AU - Benhui CHEN
AU - Jinglu HU
PY - 2010
DO - 10.1587/transfun.E93.A.1792
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2010
AB - For optimization problems with irregular and complex multimodal landscapes, Estimation of Distribution Algorithms (EDAs) suffer from the drawback of premature convergence similar to other evolutionary algorithms. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine the searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by using a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first one is solving three benchmark functional multimodal optimization problems by a continuous EDA based on single Gaussian probabilistic model; the other one is solving a real complicated discrete EDA optimization problem, the HP model protein folding based on k-order Markov probabilistic model. Simulation results show that the proposed adaptive niching EDA is an efficient method.
ER -