A new approach to the Population-Based Incremental Learning algorithm using virtual regions for task mapping on NoCs

2019 ◽  
Vol 97 ◽  
pp. 443-454 ◽  
Author(s):  
L.G. García Morales ◽  
J.E. Aedo Cobo ◽  
N. Bagherzadeh
Keyword(s):  
Incremental Learning ◽  
Learning Algorithm ◽  
Population Based ◽  
Task Mapping ◽  
New Approach

A diversity maintaining population-based incremental learning algorithm

2008 ◽  
Vol 178 (21) ◽  
pp. 4038-4056 ◽  
Author(s):  
Mario Ventresca ◽  
Hamid R. Tizhoosh
Keyword(s):  
Incremental Learning ◽  
Learning Algorithm ◽  
Population Based

A Hybrid Population-Based Incremental Learning Algorithm for M-Machine Reentrant Permutation Flow-Shop Scheduling

2013 ◽  
Vol 655-657 ◽  
pp. 1636-1641
Author(s):  
Zuo Cheng Li ◽  
Bin Qian ◽  
Rong Hu ◽  
Xiao Hong Zhu
Keyword(s):  
Incremental Learning ◽  
Flow Shop ◽  
Learning Algorithm ◽  
Population Based ◽  
Flow Shop Scheduling ◽  
Hybrid Population ◽  
Neighborhood Search ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

In this paper, a hybrid population-based incremental learning algorithm (HPBIL) is proposed for solving the m-machine reentrant permutation flow-shop scheduling problem (MRPFSSP). The objective function is to minimize the maximum completion time (i.e., makespan). In HPBIL, the PBIL with a proposed Insert-based mutation is used to perform global exploration, and an Interchange-based neighborhood search with first move strategy is designed to enhance the local exploitation ability. Computational experiments and comparisons demonstrate the effectiveness of the proposed HPBIL.

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