A probabilistic memetic framework
Author
Nguyen, Quang Huy
Ong, Yew Soon
Lim, Meng-Hiot
Date of Issue
2009School
School of Electrical and Electronic Engineering
Version
Published version
Abstract
Memetic algorithms (MAs) represent one of the
recent growing areas in evolutionary algorithm (EA) research.
The term MAs is now widely used as a synergy of evolutionary or
any population-based approach with separate individual learning
or local improvement procedures for problem search. Quite often,
MAs are also referred to in the literature as Baldwinian EAs,
Lamarckian EAs, cultural algorithms, or genetic local searches.
In the last decade, MAs have been demonstrated to converge
to high-quality solutions more efficiently than their conventional
counterparts on a wide range of real-world problems. Despite the
success and surge in interests on MAs, many of the successful
MAs reported have been crafted to suit problems in very specific
domains. Given the restricted theoretical knowledge available in
the field of MAs and the limited progress made on formal MA
frameworks, we present a novel probabilistic memetic framework
that models MAs as a process involving the decision of embracing
the separate actions of evolution or individual learning and
analyzing the probability of each process in locating the global
optimum. Further, the framework balances evolution and individual
learning by governing the learning intensity of each individual
according to the theoretical upper bound derived while the search
progresses. Theoretical and empirical studies on representative
benchmark problems commonly used in the literature are presented
to demonstrate the characteristics and efficacies of the
probabilistic memetic framework. Further, comparisons to recent
state-of-the-art evolutionary algorithms, memetic algorithms, and
hybrid evolutionary-local search demonstrate that the proposed
framework yields robust and improved search performance.
Subject
DRNTU::Engineering::Electrical and electronic engineering
Type
Journal Article
Series/Journal Title
IEEE transactions on evolutionary computation
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http://dx.doi.org/10.1109/TEVC.2008.2009460
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