GenHap: A novel computational method based on genetic algorithms for haplotype assembly

The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composed of randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process. Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.

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GenHap: A novel computational method based on genetic algorithms for haplotype assembly

10.7287/peerj.preprints.3246v1 ◽

2017 ◽

Cited By ~ 1

Author(s):

Andrea Tangherloni ◽

Simone Spolaor ◽

Leonardo Rundo ◽

Marco S Nobile ◽

Ivan Merelli ◽

...

Keyword(s):

Genetic Algorithms ◽

Selection Process ◽

Fitness Function ◽

Computational Method ◽

Nucleotide Polymorphisms ◽

Optimization Strategy ◽

Genetic Operators ◽

Chromosome Conformation ◽

Haplotype Assembly ◽

Fitness Value

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Assembly Planning: A Genetic Approach

Volume 2: 24th Design Automation Conference ◽

10.1115/detc98/dac-5798 ◽

1998 ◽

Author(s):

Shiang-Fong Chen

Keyword(s):

Genetic Algorithms ◽

Fitness Function ◽

Assembly Planning ◽

Genetic Approach ◽

Genetic Operators ◽

Complex Problems ◽

All Solutions ◽

Assembly Problem ◽

Optimal Assembly

Abstract The difficulty of an assembly problem is the inherent complexity of possible solutions. If the most suitable plan is selected after all solutions are found, it will be very time consuming and unrealistic. Motivated by the success of genetic algorithms (GAs) in solving combinatorial and complex problems by examining a small number of possible candidate solutions, GAs are employed to find a near-optimal assembly plan for a general environment. Five genetic operators are used: tree crossover, tree mutation, cut-and-paste, break-and-joint, and reproduction. The fitness function can adapt to different criteria easily. This assembly planner can help an inexperienced technician to find a good solution efficiently. The algorithm has been fully implemented. One example product is given to show the applications and results.

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Analysis of Selection Methods Used in Genetic Algorithms

NaUKMA Research Papers Computer Science ◽

10.18523/2617-3808.2021.4.29-43 ◽

2021 ◽

Vol 4 ◽

pp. 29-43

Author(s):

Nataliya Gulayeva ◽

Artem Ustilov

Keyword(s):

Growth Rate ◽

Genetic Algorithms ◽

Selection Pressure ◽

Fitness Function ◽

Selection Method ◽

Initial Population ◽

Reproduction Rate ◽

Selection Methods ◽

Selection Scheme ◽

Fitness Value

This paper offers a comprehensive review of selection methods used in the generational genetic algorithms.Firstly, a brief description of the following selection methods is presented: fitness proportionate selection methods including roulette-wheel selection (RWS) and its modifications, stochastic remainder selection with replacement (SRSWR), remainder stochastic independent selection (RSIS), and stochastic universal selection (SUS); ranking selection methods including linear and nonlinear rankings; tournament selection methods including deterministic and stochastic tournaments as well as tournaments with and without replacement; elitist and truncation selection methods; fitness uniform selection scheme (FUSS).Second, basic theoretical statements on selection method properties are given. Particularly, the selection noise, selection pressure, growth rate, reproduction rate, and computational complexity are considered. To illustrate selection method properties, numerous runs of genetic algorithms using the only selection method and no other genetic operator are conducted, and numerical characteristics of analyzed properties are computed. Specifically, to estimate the selection pressure, the takeover time and selection intensity are computed; to estimate the growth rate, the ratio of best individual copies in two consecutive populations is computed; to estimate the selection noise, the algorithm convergence speed is analyzed based on experiments carried out on a specific fitness function assigning the same fitness value to all individuals.Third, the effect of selection methods on the population fitness distribution is investigated. To do this, there are conducted genetic algorithm runs starting with a binomially distributed initial population. It is shown that most selection methods keep the distribution close to the original one providing an increased mean value of the distribution, while others (such as disruptive RWS, exponential ranking, truncation, and FUSS) change the distribution significantly. The obtained results are illustrated with the help of tables and histograms.

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Optimasi Pendistribusian Kelas Pada Dosen di STMIK STIKOM Indonesia Menggunakan Algoritma Genetika

Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) ◽

10.33173/jsikti.49 ◽

2019 ◽

Vol 2 (1) ◽

pp. 145-154

Author(s):

Aniek Suryanti Kusuma ◽

Komang Sri Aryati

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Genetic Parameters ◽

Fitness Function ◽

Genetic Process ◽

Fitness Value ◽

Distribution Process ◽

Class Scheduling ◽

System Requirements

The stage of class scheduling starts from scheduling courses in classes, then distributing the class to lecturers. The process of distributing classes to lecturers becomes an obstacle for the STMIK STIKOM Indonesia academic body because the academic body must adjust the existing class with the lecturer who is interested in it as well as the lecturer chosen to support a class so that it does not have classes that have a time conflict. One method for solving these problems is by using genetic algorithms that work by generating a number of random solutions and then processing the collection of solutions in a genetic process. There are eight genetic algorithm procedures, which are random chromosome generation procedures, chromosome repair to validate chromosomes from their limits, fitness function to calculate the feasibility of a solution, crossover, mutation, child repair and elitism. The output of this research is in the form of an analysis and determination of the system requirements that must exist. In addition, it produces a trial report on the effect of genetic parameters to determine the effect of changes in the value of genetic parameters on the fitness value and the time used to carry out the distribution process.

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Applications of Genetic Algorithms to the Determination of Reaction Mechanisms

Determination of Complex Reaction Mechanisms ◽

10.1093/oso/9780195178685.003.0012 ◽

2006 ◽

Author(s):

John Ross ◽

Igor Schreiber ◽

Marcel O. Vlad

Keyword(s):

Genetic Algorithms ◽

Rate Coefficient ◽

Optimization Problems ◽

Chemical Species ◽

Optimization Methods ◽

Computational Method ◽

Rate Coefficients ◽

Next Generation ◽

Binary Coding ◽

Fitness Value

The mathematical computational method of genetic algorithms is frequently useful in solving optimization problems in systems with many parameters, for example, a search for suitable parameters of a given problem that achieves a stated purpose. The method searches for these parameters in an efficient parallel way, and has some analogies with evolution. There are other optimization methods available, such as stimulated annealing, but we shall use genetic algorithms. We shall present three different problems that give an indication of the diversity of applications. We begin with a very short primer on genetic algorithms, which can be omitted if the reader has some knowledge of this subject. Genetic algorithms (GAs) work with a coding of a parameter set, which in the field of chemical kinetics may consist of a number of parameters, such as rate coefficients; variables and constraints, such as concentrations; and other quantities such as chemical species. Binary coding for a parameter is done as follows. Suppose we have a rate coefficient = 9.08 × 10−7; then if we write that rate coefficient as 10−P , with −10 ≤ P ≤ 10, a binary coding with string length of 16 bits is given by . . . P = 10 − 20 R /(216 − 1) (10.1) . . . where 0 ≤ R ≤ 216 − 1. Since P = 6.04 we have R = 12,971, or R = 0011001010101010 to the base 2. Thus the value of the rate coefficient is encoded in a single bit string, called a chromosome. For the solution of a given problem an optimization criterion must be chosen. With a given choice of parameters this criterion is calculated; the comparison of that calculation with the goal set for the criterion gives a fitness value for that set of parameters. If the fitness is adequate but not sufficient, when both are selected by prior choice, for any individual, then retain that individual for the next generation. Reject individuals below that choice. Select individuals for the next generation with a probability proportional to the fitness value from a roulette wheel on which the slot size is proportional to the fitness value. Notice that genetic algorithms use probabilistic, not deterministic, transition rules.

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Specific Evolutionary Algorithms

Evolutionary Algorithms in Theory and Practice ◽

10.1093/oso/9780195099713.003.0007 ◽

1996 ◽

Author(s):

Thomas Bäck

Keyword(s):

Genetic Algorithms ◽

Evolutionary Algorithms ◽

Selection Process ◽

Evolutionary Programming ◽

Main Stream ◽

Genetic Operators ◽

Optimization Task ◽

Main Components ◽

High Level

In this chapter, an outline of an Evolutionary Algorithm is formulated that is sufficiently general to cover at least the three different main stream algorithms mentioned before, namely, Evolution Strategies, Genetic Algorithms, and Evolutionary Programming. As in the previous chapter, algorithms are formulated in a language obtained by mixing pseudocode and mathematical notations, thus allowing for a high-level description which concentrates on the main components. These are: A population of individuals which is manipulated by genetic operators — especially mutation and recombination, but others may also be incorporated — and undergoes a fitness-based selection process, where fitness of an individual depends on its quality with respect to the optimization task.

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Improved Genetic Algorithms for Software Testing Cases Generation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.1464 ◽

2013 ◽

Vol 380-384 ◽

pp. 1464-1468

Author(s):

Shun Kun Yang ◽

Fu Ping Zeng

Keyword(s):

Genetic Algorithms ◽

Software Testing ◽

Fitness Function ◽

Automatic Generation ◽

Improved Genetic Algorithm ◽

Dynamic Adjustment ◽

Genetic Operators ◽

Adaptive Genetic Algorithms ◽

Crossover And Mutation ◽

Testing Coverage

In order to realize the adaptive Genetic Algorithms to balance the contradiction between algorithm convergence rate and algorithm accuracy for automatic generation of software testing cases, improved Genetic Algorithms is proposed for different aspects. Orthogonal method and Equivalence partitioning are employed together to make the initial testing population more effective with more reasonable coverage; Genetic operators of Crossover and Mutation is defined adaptively by the dynamic adjustment according to multi-objective Fitness function, which can guide the testing process more properly and realize the biggest testing coverage to find more defects as far as possible. Finally, the improved Genetic Algorithm are compared and analyzed by testing one benchmark program to verify its feasibility and effectiveness.

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Optimasi Asupan GGL Ideal Pada Usia Produktif Dengan Algoritma Genetika

Indonesian Journal of Applied Informatics ◽

10.20961/ijai.v4i2.41505 ◽

2020 ◽

Vol 4 (2) ◽

pp. 145

Author(s):

Anita Sindar RM Sinaga

Keyword(s):

Fast Food ◽

Eating Habits ◽

Selection Process ◽

Fitness Function ◽

Optimal Solution ◽

Adaptive Method ◽

A Value ◽

Candidate Solution ◽

Fitness Value ◽

Reproduction Process

<table border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p><em>Financial security</em><em> </em><em>encourages fast food eating habits, the characteristics of problems that require solving genetic algorithms that have multi-objective and multi-criteria. Based on the mathematical model built, an analysis is performed to find the best (optimal) solution. Optimization is an effort or activity to get the best results with the requirements given. Genetic Algorithm as a branch of Evolution Algorithm is an adaptive method commonly used to solve a value search in an optimization problem. To check the results of the optimization we need a fitness function, which signifies a coded description of the solution. During the process, the parent must be used for reproduction, crossing and mutation to obtain new offspring. Determination of the composition of the ideal GGL for productive age must meet the minimum limits for each component of nutrition. The higher the Fitness value the better the chromosomes become a candidate solution. Offspring results generated from the results of the reproduction process are crossever and mutation. The selection process is carried out to obtain the best chromosomes that will be made into the next generation's population.</em><em> </em><em>The best chromosomes offSpring 10 Fitness 12737.34.</em></p></td></tr></tbody></table>

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Optimisation of Rice Fertiliser Composition using Genetic Algorithms

Knowledge Engineering and Data Science ◽

10.17977/um018v2i22019p72-81 ◽

2019 ◽

Vol 2 (2) ◽

pp. 72

Author(s):

Retno Dewi Anissa ◽

Wayan Firdaus Mahmudy ◽

Agus Wahyu Widodo

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Selection Process ◽

Test Results ◽

Random Mutation ◽

Rice Quality ◽

Food Scarcity ◽

Fitness Value ◽

Reproduction Process ◽

Tournament Selection

There are so many problems with food scarcity. One of them is not too good rice quality. So, an enhancement in rice production through an optimal fertiliser composition. Genetic algorithm is used to optimise the composition for a more affordable price. The process of genetic algorithm is done by using a representation of a real code chromosome. The reproduction process using a one-cut point crossover and random mutation, while for the selection using binary tournament selection process for each chromosome. The test results showed the optimum results are obtained on the size of the population of 10, the crossover rate of 0.9 and the mutation rate of 0.1. The amount of generation is 10 with the best fitness value is generated is equal to 1,603.

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