An Empirical Study of the Effect of Parameter Combination on the Performance of Genetic Algorithms

2013 ◽  
Vol 1 (2) ◽  
pp. 43-55
Author(s):  
Pi-Sheng Deng
Keyword(s):  
Genetic Algorithms ◽  
Empirical Study ◽  
Population Size ◽  
Mutation Rate ◽  
Design Guidelines ◽  
Selection System ◽  
Genetic Operators ◽  
Research Results ◽  
Parameter Combination ◽  
Determinant Factor

Performance of genetic algorithms is affected not only by each genetic operator, but also by the interaction among genetic operators. Research on this issue still fails to converge to any conclusion. In this paper, the author focuses mainly on investigating, through a series of systematic experiments, the effects of different combinations of parameter settings for genetic operators on the performance of the author’s GA-based batch selection system, and compare the research results with the claims made by previous research. One of the major findings of the author’s research is that the crossover rate is not as a determinant factor as the population size or the mutation rate in affecting a GA’s performance. This paper intends to serve as an inquiry into the research of useful design guidelines for parameterizing GA-based systems.

A Study of the Performance Effect of Genetic Operators

2011 ◽  
pp. 1504-1509
Author(s):  
Pi-Sheng Deng
Keyword(s):  
Genetic Algorithms ◽  
Optimal Parameter ◽  
Research Problem ◽  
Design Guidelines ◽  
Selection System ◽  
Genetic Operators ◽  
Performance Effect ◽  
Complicated Manner ◽  
Series Of Experiments ◽  
Universally Optimal

Performance of genetic algorithms (GAs) is mainly determined by several factors. Not only the genetic operators affect the performance of a GA with varying degrees, but also the parameter settings for genetic operators interact in a complicated manner with each other in influencing a GA’s performance. Though many studies have been conducted for this cause, they failed to converge to consistent conclusions regarding the importance of different genetic operators and their parameter settings on the performance of GAs. Actually, optimizing the combinations of different strategies and parameters for different problem types is an NPcomplete problem in itself, and is still an open research problem for GAs (Mitchell, 1996). Recognizing the intrinsic difficulties in finding universally optimal parameter configurations for different classes of problems, we advocate the experience-based approach to discovering generalized guiding rules for different problem domains. To this end, it is necessary for us to gain a better understanding about how different genetic operators and their parameter combinations affect a GA’s behavior. In this research, we systematically investigate, through a series of experiments, the effect of GA operators and the interaction among GA operators on the performance of the GA-based batch selection system as proposed in Deng (2007). This paper intends to serve as an initial inquiry into the research of useful design guidelines for configuring GA-based systems.

scholarly journals Optimization of Vehicle Routing Problem with Time Window (VRPTW) for Food Product Distribution Using Genetics Algorithm

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Rayandra Yala Pratama ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithms ◽  
Population Size ◽  
Vehicle Routing ◽  
Mutation Rate ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Food Distribution ◽  
Genetic Operators ◽  
Routing Problem ◽  
Crossover And Mutation

Food distribution process is very important task because the product can expire during distribution and the further the distance the greater the cost. Determining the route will be more difficult if all customers have their own time to be visited. This problem is known as the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW problems can be solved using genetic algorithms because genetic algorithms generate multiple solutions at once. Genetic algorithms generate chromosomes from serial numbers that represent the customer number to visit. These chromosomes are used in the calculation process together with other genetic operators such as population size, number of generations, crossover and mutation rate. The results show that the best population size is 300, 3,000 generations, the combination of crossover and mutation rate is 0.4:0.6 and the best selection method is elitist selection. Using a data test, the best parameters give a good solution that minimize the distribution route.

Performance Evaluation of WMN-GA Simulation System for Different Settings of Genetic Operators Considering Giant Component and Number of Covered Users

2012 ◽  
Vol 3 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Admir Barolli ◽  
Makoto Takizawa ◽  
Tetsuya Oda ◽  
Evjola Spaho ◽  
Leonard Barolli ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Performance Evaluation ◽  
Mutation Rate ◽  
Simulation System ◽  
Giant Component ◽  
Genetic Operators ◽  
Crossover Rate ◽  
Size Of Giant Component ◽  
User Coverage ◽  
Simulation Results

In this paper, the authors propose and implement a system based on Genetic Algorithms (GAs) called WMN-GA. They evaluated the performance of WMN-GA for 0.7 crossover rate and 0.3 mutation rate, exponential ranking and different distribution of clients considering size of giant component and number of covered users parameters. The simulation results show that for normal distribution the system has better performance. The authors also carried out simulations for 0.8 crossover rate and 0.2 mutation rate. The simulation results show that the setting for 0.7 crossover rate and 0.3 mutation rate offers better connectivity and user coverage.

Multiple von Neumann Computers: An Evolutionary Approach to Functional Emergence

1997 ◽  
Vol 3 (2) ◽  
pp. 121-142 ◽  
Author(s):  
Hideaki Suzuki
Keyword(s):  
Genetic Algorithms ◽  
Population Size ◽  
Mutation Rate ◽  
Processing Unit ◽  
Von Neumann ◽  
Central Processing ◽  
Machine Instruction ◽  
Program Memory ◽  
Program Diversity ◽  
Evolutionary Speed

A novel system composed of multiple von Neumann computers and an appropriate problem environment is proposed and simulated. Each computer has a memory to store the machine instruction program, and when a program is executed, a series of machine codes in the memory is sequentially decoded, leading to register operations in the central processing unit (CPU). By means of these operations, the computer not only can handle its generally used registers but also can read and write the environmental database. Simulation is driven by genetic algorithms (GAs) performed on the population of program memories. Mutation and crossover create program diversity in the memory, and selection facilitates the reproduction of appropriate programs. Through these evolutionary operations, advantageous combinations of machine codes are created and fixed in the population one by one, and the higher function, which enables the computer to calculate an appropriate number from the environment, finally emerges in the program memory. In the latter half of the article, the performance of GAs on this system is studied. Under different sets of parameters, the evolutionary speed, which is determined by the time until the domination of the final program, is examined and the conditions for faster evolution are clarified. At an intermediate mutation rate and at an intermediate population size, crossover helps create novel advantageous sets of machine codes and evidently accelerates optimization by GAs.

THE ROLE OF MUTATION AND POPULATION SIZE IN GENETIC ALGORITHMS APPLIED TO PHYSICS PROBLEMS

2001 ◽  
Vol 12 (09) ◽  
pp. 1345-1355 ◽  
Author(s):  
E. BELMONT-MORENO
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Population Size ◽  
Mutation Rate ◽  
Test Problems ◽  
Optimum Performance ◽  
Analytical Expressions ◽  
Standard Genetic Algorithm ◽  
Physics Problems

A standard Genetic Algorithm is applied to a set of test problems, three of them taken from physics and the rest analytical expressions explicitly constructed to test search procedures. The relation between mutation rate and population size in the search for optimum performance is obtained showing similar behavior in these problems.

scholarly journals A phylogenetic estimator of effective population size or mutation rate.

Genetics ◽  
1994 ◽  
Vol 136 (2) ◽  
pp. 685-692 ◽  
Author(s):  
Y X Fu
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Mutation Rate ◽  
Dna Sequences ◽  
High Efficiency ◽  
Minimum Variance ◽  
Population Subdivision ◽  
Effective Population ◽  
Fisher Model ◽  
Mitochondrial Sequences

Abstract A new estimator of the essential parameter theta = 4Ne mu from DNA polymorphism data is developed under the neutral Wright-Fisher model without recombination and population subdivision, where Ne is the effective population size and mu is the mutation rate per locus per generation. The new estimator has a variance only slightly larger than the minimum variance of all possible unbiased estimators of the parameter and is substantially smaller than that of any existing estimator. The high efficiency of the new estimator is achieved by making full use of phylogenetic information in a sample of DNA sequences from a population. An example of estimating theta by the new method is presented using the mitochondrial sequences from an American Indian population.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

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