Paper Info
Title
A Genetic Algorithm for Physical Mapping Problems
Abstract
In this paper, we study a genetic algorithm for solving physical mapping problem. First, the physical mapping problem is transferred to an optimization problem by incorporating biological knowledge and limitations into the objective function. Based on the idea of genetic algorithms, the proposed approach integrates Edge Assembly Crossover (EAX) and Inver-over genetic operators to get the optimal solution. We analyze essential components of the proposed approach as well as implementation details. Our approach is then applied to some widely used test sets and simulated data, real data of this problem. Experimental results indicate that the new approach performs efficiently and precisely to solve physical mapping problem.
Year
Venue
Keywords
2000
GECCO
genetic operator,objective function,optimization problem,genetic algorithm
Field
DocType
Citations 
Genetic operator,Mathematical optimization,Crossover,Computer science,Meta-optimization,Genetic representation,Cultural algorithm,Population-based incremental learning,Optimization problem,Genetic algorithm
Conference
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Huai-Kuang Tsai113214.33
Cheng-yan Kao258661.50
Jinn-moon Yang336435.89