A parallel genetic algorithm to speed up the resolution of the algorithm selection problem

2021 ◽  
Author(s):  
Alejandro Marrero ◽  
Eduardo Segredo ◽  
Coromoto Leon
Keyword(s):  
Genetic Algorithm ◽  
Selection Problem ◽  
Parallel Genetic Algorithm ◽  
Algorithm Selection ◽  
Speed Up

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2015 ◽  
pp. 334-347 ◽  
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

Master-Slave Parallel Genetic Algorithm Based on MapReduce Using Cloud Computing

2011 ◽  
Vol 121-126 ◽  
pp. 4023-4027 ◽  
Author(s):  
Guang Ming Li ◽  
Wen Hua Zeng ◽  
Jian Feng Zhao ◽  
Min Liu
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
High Performance ◽  
Low Cost ◽  
Parallel Genetic Algorithm ◽  
Parallel Genetic Algorithms ◽  
Computing Platform ◽  
Speed Up ◽  
Cloud Computing Platform ◽  
High Performance Computer

The implementation platforms of parallel genetic algorithms (PGAs) include high performance computer, cluster and Grid. Contrast with the traditional platform, a Master-slave PGA based on MapReduce (MMRPGA) of cloud computing platform was proposed. Cloud computing is a new computer platform, suites for larger-scale computing and is low cost. At first, describes the design of MMRPGA, in which the whole evolution is controlled by Master and the fitness computing is assigned to Slaves; then deduces the theoretical speed-up of MMRPGA; at last, implements MMRPGA on Hadoop and compares the speed-up with traditional genetic algorithm, the experiment result shows MMRPGA can achieve slightly lower linear speed-up with Mapper’s number.

scholarly journals Construct Linear Polynomial Complementary Transformation for NP-Completeness Using Parallel Genetic Algorithm

2016 ◽  
Author(s):  
Tarik Eltaeib ◽  
Julius Dichter
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Genetic Algorithm ◽  
Algorithm Optimization ◽  
Parallel Genetic Algorithms ◽  
Np Completeness ◽  
Linear Polynomial ◽  
Optimization Function ◽  
Speed Up ◽  
Complementary Equation

This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.

scholarly journals Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2016 ◽  
pp. 1071-1084
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

Simple Parallel Genetic Algorithm Using Cloud Computing

2011 ◽  
Vol 121-126 ◽  
pp. 4151-4155 ◽  
Author(s):  
Jian Feng Zhao ◽  
Wen Hua Zeng ◽  
Guang Ming Li ◽  
Min Liu
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Open Source ◽  
High Performance ◽  
Parallel Genetic Algorithm ◽  
Communication Time ◽  
Speed Up ◽  
Parallel Platform ◽  
High Performance Computers

Cloud computing is a novel parallel platform, this paper proposed a kind of simple parallel genetic algorithm (PGA) using Cloud computing called SMRPGA. Comparing with the traditional PGAs using high performance computers (HPC), cluster or Grid, SMRPGA is simple and easy to be implemented. Another advantage is that PGA using Cloud computing is easy to be extend to larger-scale, which is very useful for solving the time-consuming problems. A prototype is implemented based on Hadoop, which is an open source Cloud computing. The result of running two benchmark functions showed that the speed-up of PGA using Cloud Computing is not obvious considering the long communication time and it is suitable to solve the time-consuming problems.

Research on Computer Networks Reliability Optimization by Parallel Genetic Algorithms Method

2014 ◽  
Vol 543-547 ◽  
pp. 2984-2987
Author(s):  
Xu Cao ◽  
Jun Pan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Network Topology ◽  
Coarse Grained ◽  
Reliability Optimization ◽  
Parallel Genetic Algorithm ◽  
Parallel Genetic Algorithms ◽  
Cost Constraints ◽  
Speed Up

This paper can be asserted that the use of parallel genetic algorithm can not only effectively improve the calculation speed and optimize the quality, but also can improve a lot of advantages. Reliability optimization for computer net works, subject s to cost constraints, is a NP-hard combinational problem. Reg ar ding a known network topology, the problem of choosing links and switchers among alternatives different in reliability and cost is settled by a Coarse-grained parallel genetic algorithm, which maximize the network availablity within a fixed budget. T he simulations on a dedicated cluster demonst rate that contracting to the sequential counterpart, o ur par allel GA improves the quality of plans greatly with an evident speed-up.

Research on text feature clustering based on an improved parallel genetic algorithm

2015 ◽  
Author(s):  
M. Y. Jiang ◽  
X. J. Fan ◽  
Y. X. Zhou ◽  
J. Lian ◽  
J. Q. Jiang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Parallel Genetic Algorithm ◽  
Feature Clustering ◽  
Text Feature
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