An adaptive learning automata-based ranking function discovery algorithm

2012 ◽  
Vol 39 (2) ◽  
pp. 441-459 ◽  
Author(s):  
Javad Akbari Torkestani
Keyword(s):  
Adaptive Learning ◽  
Learning Automata ◽  
Ranking Function ◽  
Function Discovery

Genetic-based approaches in ranking function discovery and optimization in information retrieval — A framework

2009 ◽  
Vol 47 (4) ◽  
pp. 398-407 ◽  
Author(s):  
Weiguo Fan ◽  
Praveen Pathak ◽  
Mi Zhou
Keyword(s):  
Information Retrieval ◽  
Ranking Function ◽  
Function Discovery

A generic ranking function discovery framework by genetic programming for information retrieval

2004 ◽  
Vol 40 (4) ◽  
pp. 587-602 ◽  
Author(s):  
Weiguo Fan ◽  
Michael D Gordon ◽  
Praveen Pathak
Keyword(s):  
Information Retrieval ◽  
Genetic Programming ◽  
Ranking Function ◽  
Function Discovery

scholarly journals Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881504 ◽  
Author(s):  
Hongliang Zhu ◽  
Zhihua Zhang ◽  
Juan Du ◽  
Shoushan Luo ◽  
Yang Xin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Adaptive Learning ◽  
Packet Loss ◽  
Learning Automata ◽  
Attack Detection ◽  
Wireless Sensor ◽  
Communication Quality ◽  
Selective Forwarding

Wireless sensor networks face threats of selective forwarding attacks which are simple to implement but difficult to detect. It is difficult to distinguish between malicious packet dropping and the normal packet loss on unstable wireless channels. For this situation, a selective forwarding attack detection method is proposed based on adaptive learning automata and communication quality; the method can eliminate the impact of normal packet loss on selective forwarding attack detection and can detect ordinary selective forwarding attack and special cases of selective forwarding attack. The current and comprehensive communication quality of nodes are employed to reflect the short- and long-term forwarding behaviors of nodes, and the normal packet loss caused by unstable channels and medium-access-control layer collisions is considered. The adaptive reward and penalty parameters of a detection learning automata are determined by the comprehensive communication quality of the node and the voting of its neighbors to reward normal nodes or punish malicious ones. Simulation results indicate the effectiveness of the proposed method in detecting ordinary selective forwarding attacks, black-hole attacks, on-off attacks, and energy exhaustion attacks. In addition, the communication overhead of the method is lower than that of other methods.

An immune programming-based ranking function discovery approach for effective information retrieval

2010 ◽  
Vol 37 (8) ◽  
pp. 5863-5871 ◽  
Author(s):  
Shuaiqiang Wang ◽  
Jun Ma ◽  
Qiang He
Keyword(s):  
Information Retrieval ◽  
Ranking Function ◽  
Immune Programming ◽  
Function Discovery

An adaptive learning to rank algorithm: Learning automata approach

2012 ◽  
Vol 54 (1) ◽  
pp. 574-583 ◽  
Author(s):  
Javad Akbari Torkestani
Keyword(s):  
Adaptive Learning ◽  
Learning To Rank ◽  
Learning Automata

Dynamic Channel Allocation in Wireless Networks Using Adaptive Learning Automata

2011 ◽  
Vol 18 (4) ◽  
pp. 295-308 ◽  
Author(s):  
Behdis Eslamnour ◽  
S. Jagannathan ◽  
Maciej J. Zawodniok
Keyword(s):  
Wireless Networks ◽  
Adaptive Learning ◽  
Channel Allocation ◽  
Learning Automata ◽  
Dynamic Channel Allocation ◽  
Dynamic Channel
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