A Memetic Algorithm Configured Via a Problem Solving Environment for the Hamiltonian Cycle Problems

X. S. Chen; Meng-Hiot Lim; Donald C. Wunsch

doi:10.1109/CEC.2007.4424821

A Memetic Algorithm Configured Via a Problem Solving Environment for the Hamiltonian Cycle Problems

X. S. Chen
, Meng-Hiot Lim
, Donald C. Wunsch

Research output: Contribution to journal › Article › peer-review

Abstract

Algorithm Development Environment for Permutation-based problems (ADEP) is a software environment for configuring meta-heuristics for solving combinatorial optimization problems. This paper describes the key features of ADEP and how the environment was used to generate a Memetic Algorithm (MA) solution for Hamiltonian Cycle Problems (HCP). The effectiveness of the MA algorithm is demonstrated through computer simulations and its performance is compared with backtracking and other heuristic techniques such as Simulated Annealing, Tabu Search, and Ant Colony Optimization.

Original language	American English
Journal	Proceedings of the IEEE Congress on Evolutionary Computation, 2007
DOIs	https://doi.org/10.1109/CEC.2007.4424821
State	Published - Sep 1 2007

Keywords

Evolutionary Computation
Graph Theory
Mathematics Computing
Optimization

Disciplines

Electrical and Computer Engineering

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10.1109/CEC.2007.4424821License: Unspecified

A Memetic Algorithm Configured Via a Problem Solving EnvironmentFinal published version, 442 KBLicense: Unspecified

Cite this

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title = "A Memetic Algorithm Configured Via a Problem Solving Environment for the Hamiltonian Cycle Problems",

abstract = " Algorithm Development Environment for Permutation-based problems (ADEP) is a software environment for configuring meta-heuristics for solving combinatorial optimization problems. This paper describes the key features of ADEP and how the environment was used to generate a Memetic Algorithm (MA) solution for Hamiltonian Cycle Problems (HCP). The effectiveness of the MA algorithm is demonstrated through computer simulations and its performance is compared with backtracking and other heuristic techniques such as Simulated Annealing, Tabu Search, and Ant Colony Optimization.",

keywords = "Evolutionary Computation, Graph Theory, Mathematics Computing, Optimization",

author = "Chen, \{X. S.\} and Meng-Hiot Lim and Wunsch, \{Donald C.\}",

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language = "American English",

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AU - Chen, X. S.

AU - Lim, Meng-Hiot

AU - Wunsch, Donald C.

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Y1 - 2007/9/1

N2 - Algorithm Development Environment for Permutation-based problems (ADEP) is a software environment for configuring meta-heuristics for solving combinatorial optimization problems. This paper describes the key features of ADEP and how the environment was used to generate a Memetic Algorithm (MA) solution for Hamiltonian Cycle Problems (HCP). The effectiveness of the MA algorithm is demonstrated through computer simulations and its performance is compared with backtracking and other heuristic techniques such as Simulated Annealing, Tabu Search, and Ant Colony Optimization.

AB - Algorithm Development Environment for Permutation-based problems (ADEP) is a software environment for configuring meta-heuristics for solving combinatorial optimization problems. This paper describes the key features of ADEP and how the environment was used to generate a Memetic Algorithm (MA) solution for Hamiltonian Cycle Problems (HCP). The effectiveness of the MA algorithm is demonstrated through computer simulations and its performance is compared with backtracking and other heuristic techniques such as Simulated Annealing, Tabu Search, and Ant Colony Optimization.

KW - Evolutionary Computation

KW - Graph Theory

KW - Mathematics Computing

KW - Optimization

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/789

U2 - 10.1109/CEC.2007.4424821

DO - 10.1109/CEC.2007.4424821

M3 - Article

JO - Proceedings of the IEEE Congress on Evolutionary Computation, 2007

JF - Proceedings of the IEEE Congress on Evolutionary Computation, 2007

ER -

A Memetic Algorithm Configured Via a Problem Solving Environment for the Hamiltonian Cycle Problems

Abstract

Keywords

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