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Examining the relationship between algorithm stopping criteria and performance using elitist genetic algorithm
2010
Proceedings of the 2010 Winter Simulation Conference
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Thus, it is necessary to identify the tradeoff between the algorithm stopping criteria and the algorithm performance. ...
Elitist genetic algorithm is used to solve 30 projects having 30-Activity with four renewable resources for statistical analysis. ...
Figure 2: Average algorithm runtimes in millisecond for 30 projects For the comparison test of the difference among the means of algorithm runtimes by stopping criteria, one-way analysis of variance is ...
doi:10.1109/wsc.2010.5679014
dblp:conf/wsc/Kim10a
fatcat:gx4yjhpaxfbjpl2bqginms5o6i
Optimal stopping time of compact genetic algorithm on deceptive problem using real options analysis
2007
2007 IEEE Congress on Evolutionary Computation
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algorithm on the trap problem. ...
This paper proposes using a decision contour derived from real options analysis, which is an evaluation tool for investment under uncertainty, to suggest an optimal stopping time of the compact genetic ...
The proposed criterion is different from few theoretical stopping criteria of the genetic algorithms in the literature. ...
doi:10.1109/cec.2007.4425084
dblp:conf/cec/RimcharoenSC07
fatcat:xnlbyjrzlndjzhxhk4a7t23a7i
The saturation of population fitness as a stopping criterion in genetic algorithm
2019
International Journal of Electrical and Computer Engineering (IJECE)
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Genetic Algorithm is an algorithm imitating the natural evolution process in solving optimization problems. ...
The proposed stopping criteria was compared with conventional stopping criterion, fittest chromosomes repetition, under various parameters setting. ...
Section 3 details hybrid algorithms for two tested models, one with conventional stopping criterion, one with the proposed stopping criterion. ...
doi:10.11591/ijece.v9i5.pp4130-4137
fatcat:inm5mkpfuvagrfpxg2eodibsyy
The Relationship between Metaheuristics Stopping Criteria and Performances
2014
International Journal of Applied Metaheuristic Computing
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This paper addresses this issue using the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-Objective Particle Swarm Optimization with Crowding Distance (MOPSO-CD) for the model-based design ...
Indeed, it is difficult to find the best compromise between the stopping criterion and the algorithm performance. ...
Meta-Heuristics Optimization Algorithms in Engineering, metaheuristics-stopping-criteria-and-
performances/117266? ...
doi:10.4018/ijamc.2014070104
fatcat:l4vucmukfzbfrle436es3d477a
Hybrid Optimisation Method Using PGA and SQP Algorithm
2007
2007 IEEE Symposium on Foundations of Computational Intelligence
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This paper investigates the hybridisation of two very different optimisation methods, namely the Parallel Genetic Algorithm (PGA) and Sequential Quadratic Programming (SQP) Algorithm. ...
Experiments show the hybrid method effectively combines the robust and global search property of Parallel Genetic Algorithms with the high convergence velocity of the Sequential Quadratic Programming Algorithm ...
The RFSQP algorithm halts once stopping criteria has been fulfilled. ...
doi:10.1109/foci.2007.372150
dblp:conf/foci/SkinnerNL07
fatcat:z6wjpafhgvctrdveqhyskhrd4e
Determining Optimal Breakpoints in Urban Power Networks with Genetic Algorithm
2012
The Renewable Energies and Power Quality Journal (RE&PQJ)
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Determining the optimal breakpoints is a complicated discrete task, for which the method of genetic algorithm becomes the most suitable solution. ...
Devising Genetic Algorithm for Determining Optimal Breakpoints. ...
population for the present iteration of the genetic algorithm. ...
doi:10.24084/repqj10.554
fatcat:r3f22z2vobcmrmmm6o47tx2vg4
ANN-Based Stop Criteria for a Genetic Algorithm Applied to Air Impingement Design
2019
Energies
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The performance of this ANN is used as a stop criterion for the optimization process. ...
The ANN is trained with the points obtained during an optimization process by a genetic algorithm and a flower pollination algorithm. ...
Parameters
Genetic Algorithm Genetic Algorithm with Stop Criteria
Mean best Cost
33.81
33.68
Standard deviation of best Cost
0.08
0.01
Mean temperature
31.66 °C
31.42
Std of temperature
0.17 ...
doi:10.3390/en13010016
fatcat:ku3jtueztnhw3n2xft5pwdoayq
A Permutation Encoding Technique Applied to Genetic Algorithm Solution of Resource Constrained Project Scheduling Problem
2014
Nigerian Journal of Technology
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Figure 7 : 7 Arrow diagram of Project Network (Udosen, 1997)
Figure 9 :Figure 10 : 910 Solution obtained using GA for stopping criteria of 13 generations Solution obtained using Genetic Algorithm for ...
stopping criteria of 40 generations 14. ...
doi:10.4314/njt.v34i1.16
fatcat:z5s2h2fhxvejpbs326ogyyhx6a
Analysis of the optimality of the standard genetic code
2016
Molecular Biosystems
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On left, ability of genetic codes to encode additional information and their robustness to frameshift mutations. ...
Many theories have been proposed attempting to explain the origin of the genetic code. In this work, we compare performance of the standard genetic code against millions of randomly generated codes. ...
algorithm to score genetic codes for their ability to minimize frameshift errors (See methods). ...
doi:10.1039/c6mb00262e
pmid:27327359
fatcat:qxvxoewmkne5xis3ut6taz34pi
Regression Testing based on Genetic Algorithms
2016
International Journal of Computer Applications
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The work proposes a genetic algorithm based prioritization technique, which intelligently reorders the test cases on maximum fault detection rate. ...
The work paves the way of genetic algorithms in regression testing. ...
Stopping conditions for GA are maximum generations, Elapsed time and unchanged fitness [21] .
PROPOSED WORK The work proposes use of genetic algorithm in regression testing. ...
doi:10.5120/ijca2016912201
fatcat:ffgeafodv5dfnksk5azwfq7qie
A Genetic algorithm to solve the container storage space allocation problem
[article]
2013
arXiv
pre-print
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This paper presented a genetic algorithm (GA) to solve the container storage problem in the port. ...
In this paper, an adaptation of the genetic algorithm to the container storage problem is detailed and some experimental results are presented and discussed. ...
To evaluate the results of the proposed genetic algorithm, the influence of the container type number, the stopping criteria and the population size are studied.
A. ...
arXiv:1303.1051v1
fatcat:ds5reb66zzdl3kcyqnrcdbzc5m
Choreogenetics
2005
Proceedings of the 2005 workshops on Genetic and evolutionary computation - GECCO '05
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In this paper, a genetic algorithm is introduced to generate variants of a choreographic sequence, which are then selected using different criteria. ...
The mutation phase of the algorithm applies six types of mutations on single sequences, as well as four types of mutations on multiple sequences. ...
For one, a grammar-based genetic programming system may be defined to encode the rules of a particular type of dance. ...
doi:10.1145/1102256.1102338
dblp:conf/gecco/Lapointe05
fatcat:h6twi64nwfbmlj4l6ofjzjtgwi
Optimization Study For The Cross-Section Of A Concrete Gravity Dam: Genetic Algorithm Model And Application
2019
Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería
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This study focuses on the shape optimization of concrete gravity dams using genetic algorithms. ...
The optimum cross-section of a concrete gravity dam is achieved by the Genetic Algorithm (GA) through a Matlab routine developed by the author. ...
The algorithm stops when one of the stopping criteria is met. Figure 5 shown the convergence process. The GA parameters of Optdam program are shown in Table 1 . ...
doi:10.23967/j.rimni.2019.06.002
fatcat:dretnyjvnff2jlxmshusbr65yu
Dynamic Process Scheduling and Sequencing Using Genetic Algorithm
2014
IOSR Journal of Computer Engineering
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This paper present the implementation of genetic algorithm for operating system process scheduling. ...
The scheduling is considered as NP hard problem .In this paper, we use the power of genetic algorithm to provide the efficient process scheduling. ...
Is done=Optimization criteria met? Step 10. If (not) then, i=i+1.[increase iteration number]. END FOR[If optimization criteria met, stop the algorithm]. Step 11. Output the best solution END GA G. ...
doi:10.9790/0661-16394853
fatcat:w2j2zaczcfdl7cwh5sltl656cu
Minimizing total flowtime and maximum earliness on a single machine using multiple measures of fitness
2005
Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05
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The intent of this research is to investigate methods to use genetic algorithms to find the set of efficient solutions to a bi-criteria problem. ...
We investigate its performance on a specific bi-criteria scheduling problem, minimizing total flowtime and maximum earliness on a single machine. ...
We begin with a brief review of genetic algorithms and describe the random keys genetic algorithm (RKGA) in Section 2. In Section 3, we introduce a bi-criteria RKGA, BRKGA. ...
doi:10.1145/1068009.1068144
dblp:conf/gecco/KurzC05
fatcat:5jnkdrzvcbhrbftxeelwofucfi
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