Tabu Programming: a New Problem Solver through Adaptive Memory Programming over Tree Data Structures
Created by W.Langdon from
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- @Article{journals/ijitdm/HedarMF11,
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author = "Abdel-Rahman Hedar and Emad Mabrouk and
Masao Fukushima",
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title = "Tabu Programming: a New Problem Solver through
Adaptive Memory Programming over Tree Data Structures",
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journal = "International Journal of Information Technology and
Decision Making",
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volume = "10",
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number = "2",
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year = "2011",
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pages = "373--406",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1142/S0219622011004373",
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oai = "oai:RePEc:wsi:ijitdm:v:10:y:2011:i:02:p:373-406",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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abstract = "Since the first appearance of the Genetic Programming
(GP) algorithm, extensive theoretical and application
studies on it have been conducted. Nowadays, the GP
algorithm is considered one of the most important tools
in Artificial Intelligence (AI). Nevertheless, several
questions have been raised about the complexity of the
GP algorithm and the disruption effect of the crossover
and mutation operators. In this paper, the Tabu
Programming (TP) algorithm is proposed to employ the
search strategy of the classical Tabu Search algorithm
with the tree data structure. Moreover, the TP
algorithm exploits a set of local search procedures
over a tree space in order to mitigate the drawbacks of
the crossover and mutation operators. Extensive
numerical experiments are performed to study the
performance of the proposed algorithm for a set of
benchmark problems. The results of those experiments
show that the TP algorithm compares favourably to
recent versions of the GP algorithm in terms of
computational efforts and the rate of success. Finally,
we present a comprehensive framework called
Meta-Heuristics Programming (MHP) as general machine
learning tools.",
- }
Genetic Programming entries for
Abdel-Rahman Hedar
Emad H A Mabrouk
Masao Fukushima
Citations