A Novel Approach in Solving 0/1 Knapsack Problem using Neural Selection Principle

被引:0
作者
Shyamala, K. [1 ]
Chanthini, P. [1 ]
机构
[1] Univ Madras, Res Dept Comp Sci, Chennai, Tamil Nadu, India
来源
2017 IEEE INTERNATIONAL CONFERENCE ON POWER, CONTROL, SIGNALS AND INSTRUMENTATION ENGINEERING (ICPCSI) | 2017年
关键词
Feed-forward network; Recurrent network; 0/1 Knapsack Problem; Effect-Effort; Experiential Selection; Inhibitory Synapses; Parasympathetic ganglia; SHUNTING INHIBITION; NEURONAL GAIN; ALGORITHMS; NETWORKS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Artificial Replacements to human brain functionality will be possible only by constructing and testing virtual models for such functionalities. This work is the continuation of finding the possibilities to implement new neural biological theories into Artificial Neural Network (ANN). After implementation of problems with deterministic time complexity like XOR and N-Parity problem, this work is an implementation of "Neural Selection" under "Neural Darwinism" into a classic problem of non-deterministic polynomial time category Oil Knapsack Problem (KP). There have been studies about the functioning of the brain using knapsack problem. This work is the successful implementation of the exhaustive search algorithm of KP through Artificial Neural Network Model (ANNM). This implementation paves way for further modification in selection section to test heterogeneity of the model as if the human brains perform in a given situation.
引用
收藏
页码:2601 / 2605
页数:5
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