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The review of multiple evolutionary searches and multi-objective evolutionary algorithms

Cheshmehgaz, Hossein Rajabalipour and Haron, Habibollah and Sharifi, Abdollah (2015) The review of multiple evolutionary searches and multi-objective evolutionary algorithms. Artificial Intelligence Review, 43 (3). pp. 311-343. ISSN 2689-7613

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Official URL: http://dx.doi.org/10.1007/s10462-012-9378-3


Over the past decade, subdividing evolutionary search into multiple local evolutionary searches has been identified as an effective method to search for optimal solutions of multi-objective optimization problems (MOPs). The existing multi-objective evolutionary algorithms that benefit from the multiple local searches (multiple-MOEAs, or MMOEAs) use different dividing methods and/or collaborations (information sharing) strategies between the created divisions. Their local evolutionary searches are implicitly or explicitly guided toward a part of global optimal solutions instead of converging to local ones in some divisions. In this reviewed paper, the dividing methods and the collaborations strategies are reviewed, while their advantage and disadvantage are mentioned.

Item Type:Article
Uncontrolled Keywords:dividing methods, evolutionary search
Subjects:A General Works
ID Code:58988
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 12:07
Last Modified:01 Feb 2017 09:15

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