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 Modifikasi Algoritma Genetika untuk Penyelesaian Permasalahan Penjadwalan Pelajaran Sekolah

2014 ◽  
Vol 10 (1) ◽  
pp. 111
Author(s):  
Rahman Erama ◽  
Retantyo Wardoyo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Effective Algorithm ◽  
Scheduling Problems ◽  
The Core ◽  
Alternative Algorithm ◽  
Crossover Phase ◽  
School Scheduling ◽  
Core Problem ◽  
Standard Genetic Algorithm

AbstrakModifikasi Algoritma Genetika pada penelitian ini dilakukan berdasarkan temuan-temuan para peneliti sebelumnya tentang kelemahan Algoritma Genetika. Temuan-temuan yang dimakasud terkait proses crossover sebagai salah satu tahapan terpenting dalam Algoritma Genetika dinilai tidak menjamin solusi yang lebih baik oleh beberapa peneliti. Berdasarkan temuan-temuan oleh beberapa peneliti sebelumnya, maka penelitian ini akan mencoba memodifikasi Algoritma Genetika dengan mengeliminasi proses crossover yang menjadi inti permasalahan dari beberapa peneliti tersebut. Eliminasi proses crossover ini diharapkan melahirkan algoritma yang lebih efektif sebagai alternative untuk penyelesaian permasalahan khususnya penjadwalan pelajaran sekolah.Tujuan dari penelitian ini adalah Memodifikasi Algoritma Genetika menjadi algoritma alternatif untuk menyelesaikan permasalahan penjadwalan sekolah, sehingga diharapkan terciptanya algoritma alternatif ini bisa menjadi tambahan referensi bagi para peneliti untuk menyelesaikan permasalahan penjadwalan lainnya.Algoritma hasil modifikasi yang mengeliminasi tahapan crossover pada algoritma genetika ini mampu memberikan performa 3,06% lebih baik dibandingkan algoritma genetika sederhana dalam menyelesaikan permasalahan penjadwalan sekolah. Kata kunci—algoritma genetika, penjadwalan sekolah, eliminasi crossover  AbstractModified Genetic Algorithm in this study was based on the findings of previous researchers about the weakness of Genetic Algorithms. crossover as one of the most important stages in the Genetic Algorithms considered not guarantee a better solution by several researchers. Based on the findings by previous researchers, this research will try to modify the genetic algorithm by eliminating crossover2 which is the core problem of several researchers. Elimination crossover is expected to create a more effective algorithm as an alternative to the settlement issue in particular scheduling school.This study is intended to modify the genetic algorithm into an algorithm that is more effective as an alternative to solve the problems of school scheduling. So expect the creation of this alternative algorithm could be an additional resource for researchers to solve other scheduling problems.Modified algorithm that eliminates the crossover phase of the genetic algorithm is able to provide 2,30% better performance than standard genetic algorithm in solving scheduling problems school. Keywords—Genetic Algorithm, timetabling school, eliminate crossover

scholarly journals A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Adaptive Mutation ◽  
Local Optima ◽  
3 Dimensional ◽  
Successive Generations

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Novel Mutation ◽  
Fitness Function ◽  
Genetic Disorders ◽  
Optimal Solution ◽  
Global Optimum ◽  
Adaptive Mutation ◽  
Local Optima

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals Genetic Algorithm for Teaching Distribution based on Lecturers’ Expertise

2019 ◽  
pp. 297-304
Author(s):  
Rizki Agung Pambudi ◽  
Wahyuni Lubis ◽  
Firhad Rinaldi Saputra ◽  
Hanif Prasetyo Maulidina ◽  
Vivi Nur Wijayaningrum
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Mutation Rate ◽  
Learning Process ◽  
Teaching And Learning ◽  
Optimization Methods ◽  
Test Results ◽  
Optimal Parameters ◽  
Crossover Rate ◽  
Fitness Value

The teaching distribution for lecturers based on their expertise is very important in the teaching and learning process. Lecturers who teach a course that is in accordance with their interests and abilities will make it easier for them to deliver material in class. In addition, students will also be easier to accept the material presented. However, in reality, the teaching distribution is often not in accordance with the expertise of the lecturer so that the lecturers are not optimal in providing material to their students. This problem can be solved using optimization methods such as the genetic algorithm. This study offers a solution for teaching distribution that focuses on the interest of each lecturer by considering the order of priorities. The optimal parameters of the test results are crossover rate (cr) = 0.6, mutation rate (mr) = 0.4, number of generations = 40, and population size = 15. Genetic algorithm is proven to be able to produce teaching distribution solutions with a relatively high fitness value at 4903.3.

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 Species Conserving Genetic Algorithm for Multimodal Function Optimization

2002 ◽  
Vol 10 (3) ◽  
pp. 207-234 ◽  
Author(s):  
Jian-Ping Li ◽  
Marton E. Balazs ◽  
Geoffrey T. Parks ◽  
P. John Clarkson
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Multiple Solutions ◽  
Species Conservation ◽  
Function Optimization ◽  
Test Problems ◽  
Next Generation ◽  
Multimodal Function Optimization ◽  
Multimodal Function ◽  
A New Technique

This paper introduces a new technique called species conservation for evolving paral-lel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current gen-eration are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimiza-tion problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

A Genetic Engineering Approach to Genetic Algorithms

2001 ◽  
Vol 9 (1) ◽  
pp. 71-92 ◽  
Author(s):  
John S. Gero ◽  
Vladimir Kazakov
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Genetic Engineering ◽  
Genetic Material ◽  
Evolutionary Process ◽  
Automatic Generation ◽  
Engineering Approach ◽  
Standard Genetic Algorithm

We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard GA, it provides some computational advantages as well as a tool for automatic generation of hierarchical genetic representations specifically tailored to suit certain classes of problems.

scholarly journals Improving Cloud Data Encryption Using Customized Genetic Algorithm

2020 ◽  
Vol 12 (6) ◽  
pp. 46-63
Author(s):  
Muhammad Junaid Arshad ◽  
◽  
Muhammad Umair ◽  
Saima Munawar ◽  
Nasir Naveed ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Mutation Rate ◽  
Data Privacy ◽  
Data Encryption ◽  
Local Information ◽  
Sensitive Information ◽  
Secret Key ◽  
Security Breaches ◽  
Size Number

Data Encryption is widely utilized for ensuring data privacy, integrity, and confidentiality. Nowadays, a large volume of data is uploaded to the cloud, which increases its vulnerability and adds to security breaches. These security breaches include circumstances where sensitive information is being exposed to third parties or any access to sensitive information by unauthorized personnel. The objective of this research is to propose a method for improving encryption by customizing the genetic algorithm (GA) with added steps of encryption. These added steps of encryption include the data being processed with local information (chromosome's value calculated with computer-generated random bits without human intervention). The improvement in the randomness of the key generated is based on altering the population size, number of generations, and mutation rate. The first step of encrypting is to convert sample data into binary form. Once the encryption process is complete, this binary result is converted back to get the encrypted data or cipher-text. Foremost, the GA operators (population size, number of generations, and mutation rate) are changed to determine the optimal values of each operator to bring forth a random key in the minimum possible time, then local intelligence is headed in the algorithm to further improve the outcomes. Local Intelligence consists of local information and a random bit generated in each iteration. Local Information is the current value of a parent in each iteration at the gene level. Both local information and random bit are then applied in a mathematical pattern to generate a randomized key. The local intelligence-based algorithm can operate better in terms of time with the same degree of randomness that is generated with the conventional GA technique. The result showed that the proposed method is at least 80% more efficient in terms of time while generating the secret key with the same randomness level as generated by a conventional GA. Therefore, when large data are intended to be encrypted, then using local intelligence can demonstrate to be better utilized time.

scholarly journals Low genetic variation is associated with low mutation rate in the giant duckweed

2018 ◽  
Author(s):  
Shuqing Xu ◽  
Jessica Stapley ◽  
Saskia Gablenz ◽  
Justin Boyer ◽  
Klaus J. Appenroth ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Population Size ◽  
Mutation Rate ◽  
Spontaneous Mutation ◽  
Effective Population ◽  
Spontaneous Mutation Rate ◽  
Low Genetic Diversity ◽  
Genome Wide ◽  
Census Population Size

AbstractMutation rate and effective population size (Ne) jointly determine intraspecific genetic diversity, but the role of mutation rate is often ignored. We investigate genetic diversity, spontaneous mutation rate andNein the giant duckweed (Spirodela polyrhiza). Despite its large census population size, whole-genome sequencing of 68 globally sampled individuals revealed extremely low within-species genetic diversity. Assessed under natural conditions, the genome-wide spontaneous mutation rate is at least seven times lower than estimates made for other multicellular eukaryotes, whereasNeis large. These results demonstrate that low genetic diversity can be associated with large-Nespecies, where selection can reduce mutation rates to very low levels, and accurate estimates of mutation rate can help to explain seemingly counterintuitive patterns of genome-wide variation.One Sentence SummaryThe low-down on a tiny plant: extremely low genetic diversity in an aquatic plant is associated with its exceptionally low mutation rate.

scholarly journals Applicability of genetic algorithms to parameter estimation of economic models

2004 ◽  
Vol 52 (3) ◽  
pp. 79-86
Author(s):  
Marcel Ševela
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parameter Estimation ◽  
Mutation Rate ◽  
Durable Goods ◽  
Demand Function ◽  
Economic Models ◽  
Stochastic Methods ◽  
Iterative Computation ◽  
Computation Algorithm

The paper concentrates on capability of genetic algorithms for parameter estimation of non-linear economic models. In the paper we test the ability of genetic algorithms to estimate of parameters of demand function for durable goods and simultaneously search for parameters of genetic algorithm that lead to maximum effectiveness of the computation algorithm. The genetic algorithms connect deterministic iterative computation methods with stochastic methods. In the genteic aůgorithm approach each possible solution is represented by one individual, those life and lifes of all generations of individuals run under a few parameter of genetic algorithm. Our simulations resulted in optimal mutation rate of 15% of all bits in chromosomes, optimal elitism rate 20%. We can not set the optimal extend of generation, because it proves positive correlation with effectiveness of genetic algorithm in all range under research, but its impact is degreasing. The used genetic algorithm was sensitive to mutation rate at most, than to extend of generation. The sensitivity to elitism rate is not so strong.

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