Paper Info
Title
Memetic programming with adaptive local search using tree data structures
Abstract
Meta-heuristics are general frameworks of heuristics methods for solving combinatorial optimization problems, where exploring the exact solutions for these problems becomes very hard due to some limitations like extremely large running time. In this paper, new local searches over tree space are defined. Using these local searches, various meta-heuristics can be generalized to deal with tree data structures to introduce a more general framework of meta-heuristics called Meta-Heuristics Programming (MHP) as general machine learning tools. As an alternative to Genetic Programming (GP) algorithm, Memetic Programming (MP) algorithm is proposed as a new outcome of the MHP framework. The efficiency of the proposed MP Algorithm is examined through comparative numerical experiments.
Year
DOI
Venue
2008
10.1145/1456223.1456278
CSTST
Keywords
Field
DocType
genetic programming,adaptive local search,general machine,memetic programming,proposed mp algorithm,general framework,meta-heuristics programming,tree data structure,new outcome,new local search,mhp framework,local search,machine learning,evolutionary computing,iterated local search,data structure,exact solution,meta heuristics
Combinatorial optimization problem,Computer science,Tree (data structure),Evolutionary computation,Theoretical computer science,Genetic programming,Heuristics,Local search (optimization),Iterated local search,Metaheuristic
Conference
Citations 
PageRank 
References 
3
0.51
5
Authors
3
Name
Order
Citations
PageRank
Emad Mabrouk172.27
Abdel-Rahman Hedar240430.79
Masao Fukushima3232.30