A Genetic Algorithm for Solving Knapsack Problems Based on Adaptive Evolution in Dual Population

DOI

10.2991/iccia.2012.310How to use a DOI?

Keywords

Genetic algorithm, Dual population, Adaptive evalution, Knapsack problem

Abstract

In order to solve knapsack problems efficiently, an improved genetic algorithm based on adaptive evolution in dual population (called DPAGA) is proposed. In DPAGA, the new population produced by selecting operation is regarded as main population. The population composed by the individuals washed out by selecting operation is regarded as subordinate population. The individual evolution strategy of main population is different from that of subordinate population. The crossover operators and mutation operators are all adjusted non-linearly and adaptively. DPAGA is used to solve knapsack problems. The experimental results show that its convergence speed and solution quality are all better then that of simple genetic algorithm. It is also suited to solve other optimization problems.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Taishan Yan
AU  - Guanqi Guo
AU  - Hongmin Li
AU  - Wei He
PY  - 2014/05
DA  - 2014/05
TI  - A Genetic Algorithm for Solving Knapsack Problems Based on Adaptive Evolution in Dual Population
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 1252
EP  - 1254
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.310
DO  - 10.2991/iccia.2012.310
ID  - Yan2014/05
ER  -