A Developmental Evolutionary Algorithm for 0-1 Knapsack Problem

被引：0

作者：

Zhong, Ming ^{[1
]}

Xu, Bo ^{[1
]}

机构：

[1] Guangdong Univ Petrochem Technol, Dept Comp Sci & Technol, Maoming 525000, Guangdong, Peoples R China

来源：

CLOUD COMPUTING AND SECURITY, PT II | 2017年 / 10603卷

关键词：

Evolutionary computation; Developmental evolutionary theory; Reinforcement learning; Knapsack problem; OPTIMIZATION;

D O I：

10.1007/978-3-319-68542-7_77

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a developmental evolutionary algorithm (DEA) is proposed, which mainly based on the developmental evolutionary and learning theory. We regarded the chromosome individual that in EC as an autonomous development individual; and developed mental capabilities through autonomous real-time interactions with its environments by using development learning methods under the control of its intrinsic developmental program, when chromosome individual achieved the development objective, genetic operation started immediately, otherwise continue developing. Finally, we used DEA to solve the 0/1 knapsack problem and designed experiment to compare with QEA, ACO. Experimental results showed that DEA has better convergence, and can effectively avoid falling into local optimal solution.

引用

页码：849 / 854

页数：6

共 11 条

[1]

[Anonymous], 2002, The structure of evolutionary theory, DOI [DOI 10.4159/9780674417922, 10.4159/9780674417922]

[2] A model induced max-min ant colony optimization for asymmetric traveling salesman problem [J].

Bai, Jie ;

Yang, Gen-Ke ;

Chen, Yu-Wang ;

Hu, Li-Sheng ;

Pan, Chang-Chun .

APPLIED SOFT COMPUTING, 2013, 13 (03) :1365-1375

[3]

Gu B, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P3532

[4]

Hu T., 2010, J ARTIF EVOL APPL, V4, P1

[5] A belief propagation-based method for task allocation in open and dynamic cloud environments [J].

Kong, Yan ;

Zhang, Minjie ;

Ye, Dayong .

KNOWLEDGE-BASED SYSTEMS, 2017, 115 :123-132

[6] A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment [J].

Liu, Qi ;

Cai, Weidong ;

Shen, Jian ;

Fu, Zhangjie ;

Liu, Xiaodong ;

Linge, Nigel .

SECURITY AND COMMUNICATION NETWORKS, 2016, 9 (17) :4002-4012

[7] Quantum-Inspired Evolutionary Algorithm: A Multimodel EDA [J].

Platel, Michael Defoin ;

Schliebs, Stefan ;

Kasabov, Nikola .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2009, 13 (06) :1218-1232

[8] A rapid learning algorithm for vehicle classification [J].

Wen, Xuezhi ;

Shao, Ling ;

Xue, Yu ;

Fang, Wei .

INFORMATION SCIENCES, 2015, 295 :395-406

[9]

Wolpert D. H., 1997, IEEE Transactions on Evolutionary Computation, V1, P67, DOI 10.1109/4235.585893

[10] Steganalysis of least significant bit matching using multi-order differences [J].

Xia, Zhihua ;

Wang, Xinhui ;

Sun, Xingming ;

Wang, Baowei .

SECURITY AND COMMUNICATION NETWORKS, 2014, 7 (08) :1283-1291

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