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Evolutionary multi-objective optimisation with a hybrid representation

Okabe, T, Jin, Y and Sendhoff, B (2003) Evolutionary multi-objective optimisation with a hybrid representation

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Abstract

For tackling multiobjective optimisation (MOO) problem, many methods are available in the field of evolutionary computation (EC). To use the proposed method(s), the choice of the representation should be considered first. In EC, often binary representation and real-valued representation are used. We propose a hybrid representation, composed of binary and real-valued representations for multi-objective optimisation problems. Several issues such as discretisation error in the binary representation, self-adaptation of strategy parameters and adaptive switching of representations are addressed. Experiments are conducted on five test functions using six different performance indices, which shows that the hybrid representation exhibits better and more stable performance than the single binary or real-valued representation. © 2003 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Okabe, TUNSPECIFIEDUNSPECIFIED
Jin, Yyaochu.jin@surrey.ac.ukUNSPECIFIED
Sendhoff, BUNSPECIFIEDUNSPECIFIED
Date : 1 January 2003
Identification Number : https://doi.org/10.1109/CEC.2003.1299370
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:17
Last Modified : 17 May 2017 15:10
URI: http://epubs.surrey.ac.uk/id/eprint/838621

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