TY - GEN
T1 - A novel multi-population genetic algorithm for multiple-choice multidimensional knapsack problems
AU - Zhou, Qian
AU - Luo, Wenjian
PY - 2010
Y1 - 2010
N2 - In this paper, a novel Multi-Population Genetic Algorithm (MPGA) is proposed to solve the Multiple-choice Multidimensional Knapsack Problem (MMKP), a kind of classical combinatorial optimization problems. The proposed MPGA has two evolutionary populations and one archive population, and can effectively balance the search biases between the feasible space and the infeasible space. The experiment results demonstrate that the proposed MPGA is better than the existing algorithms, especially when the strength of constraints is relatively strong.
AB - In this paper, a novel Multi-Population Genetic Algorithm (MPGA) is proposed to solve the Multiple-choice Multidimensional Knapsack Problem (MMKP), a kind of classical combinatorial optimization problems. The proposed MPGA has two evolutionary populations and one archive population, and can effectively balance the search biases between the feasible space and the infeasible space. The experiment results demonstrate that the proposed MPGA is better than the existing algorithms, especially when the strength of constraints is relatively strong.
KW - Combinatorial Optimization
KW - Genetic Algorithm
KW - Multiple-choice Multidimensional Knapsack Problem
UR - https://www.scopus.com/pages/publications/78649500312
U2 - 10.1007/978-3-642-16493-4_16
DO - 10.1007/978-3-642-16493-4_16
M3 - 会议稿件
AN - SCOPUS:78649500312
SN - 3642164927
SN - 9783642164927
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 148
EP - 157
BT - Advances in Computation and Intelligence - 5th International Symposium, ISICA 2010, Proceedings
T2 - 5th International Symposium on Advances in Computation and Intelligence, ISICA 2010
Y2 - 22 October 2010 through 24 October 2010
ER -