scholarly journals An Efficient Genome Fragment Assembling Using GA with Neighborhood Aware Fitness Function

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Satoko Kikuchi ◽  
Goutam Chakraborty
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Fitness Function ◽  
Computational Cost ◽  
Search Space ◽  
Search Efficiency ◽  
Genome Sequences ◽  
Modified Genetic Algorithm ◽  
Correct Sequence ◽  
Np Problem

To decode a long genome sequence, shotgun sequencing is the state-of-the-art technique. It needs to properly sequence a very large number, sometimes as large as millions, of short partially readable strings (fragments). Arranging those fragments in correct sequence is known as fragment assembling, which is an NP-problem. Presently used methods require enormous computational cost. In this work, we have shown how our modified genetic algorithm (GA) could solve this problem efficiently. In the proposed GA, the length of the chromosome, which represents the volume of the search space, is reduced with advancing generations, and thereby improves search efficiency. We also introduced a greedy mutation, by swapping nearby fragments using some heuristics, to improve the fitness of chromosomes. We compared results with Parsons’ algorithm which is based on GA too. We used fragments with partial reads on both sides, mimicking fragments in real genome assembling process. In Parsons’ work base-pair array of the whole fragment is known. Even then, we could obtain much better results, and we succeeded in restructuring contigs covering 100% of the genome sequences.

Blade Arrangement Optimization for Mistuned Bladed Disk Based on Gaussian Process Regression and Genetic Algorithm

2020 ◽  
Vol 142 (2) ◽  
Author(s):  
Wangbai Pan ◽  
Meiyan Zhang ◽  
Guoan Tang
Keyword(s):  
Genetic Algorithm ◽  
Gaussian Process ◽  
High Efficiency ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Computational Cost ◽  
Combinatorial Problems ◽  
Optimal Arrangement ◽  
Bladed Disk ◽  
Gp Model

Abstract Mistuning phenomena exist in the bladed disk due to the inevitable deviations among blades' properties, e.g., stiffness, mass, geometry, etc., leading to localization and response amplification. The dynamic performance of mistuned bladed disk is sensitive to the arrangement of blades. The blade arrangement optimization aims to obtain the optimal arrangement that minimizes the influence of mistuning. In this paper, a framework of high efficiency is raised to deal with the challenge of high computational cost this optimization. It comprehensively utilizes mixed-dimensional finite element model (MDFEM), Gaussian process (GP) regression, and genetic algorithm (GA). The MDFEM can perform mistuned modal analysis efficiently and provides the training set of GP regression rapidly. The GP model, as a surrogate model, predicts the desired dynamic performance directly without calculating the numerical model and can function as fitness function in optimization. GA has the capability to deal with combinatorial problems and is a good option for problems with large search domains and several local maxima/minima. The techniques and processes of three methods are illustrated in detail. Case studies, based on a real turbine, are concretely presented in a gradually progressive manner to test and verify the effectiveness, accuracy, and efficiency of methods and entire framework step by step. The results show the satisfactory optimal arrangement for a randomly chosen set of mistuned blades, and the influence of mistuning is reduced indeed. The time cost of the optimization has been reduced several orders of magnitude. This framework can be a promising approach for the blade arrangement optimization problem.

scholarly journals Experimental Programming of Genetic Algorithms for the “Casse-Tête” Problem

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Search Space ◽  
Rank Test ◽  
Mutation Operators ◽  
Signed Rank ◽  
Significant Difference ◽  
Chromosome Representation ◽  
Wilcoxon Signed Rank Test ◽  
Signed Rank Test

The Casse-tête board puzzle consists of an n×n grid covered with n^2 tokens. m<n^2 tokens are deleted from the grid so that each row and column of the grid contains an even number of remaining tokens. The size of the search space is exponential. This study used a genetic algorithm (GA) to design and implement solutions for the board puzzle. The chromosome representation is a matrix of binary permutations. Variants for two crossover operators and two mutation operators were presented. The study experimented with and compared four possible operator combinations. Additionally, it compared GA and simulated annealing (SA)-based solutions, finding a 100% success rate (SR) for both. However, the GA-based model was more effective in solving larger instances of the puzzle than the SA-based model. The GA-based model was found to be considerably more efficient than the SA-based model when measured by the number of fitness function evaluations (FEs). The Wilcoxon signed-rank test confirms a significant difference among FEs in the two models (p=0.038).

scholarly journals ABOUT FEATURES OF MUTATION APPLICATION IN A MODIFIED OPERATOR GENETIC ALGORITHM

2020 ◽  
Author(s):  
Leonid Oliinyk ◽  
Stanislav Bazhan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Stochastic Matrices ◽  
Global Extremum ◽  
Modified Genetic Algorithm ◽  
Wide Range ◽  
Search Field ◽  
The Impact

Genetic algorithm is a method of optimization based on the concepts of natural selection and genetics. Genetic algorithms are used in software development, in artificial intelligence systems, a wide range of optimization problems and in other fields of knowledge.One of the important issues in the theory of genetic algorithms and their modified versions is the search for the best balance between performance and accuracy. The most difficult in this sense are problems where the fitness function in the search field has many local extremes and one global or several global extremes that coincide.The effectiveness of the genetic algorithm depends on various factors, such as the successful creation of the primary population. Also in the theory of genetic algorithms, recombination methods play an important role to obtain a better population of offspring. The aim of this work is to study some types of mutations using a modified genetic algorithm to find the minimum function of one variable.The article presents the results of research and analysis of the impact of some mutation procedures. Namely, the effect of mutation on the speed of achieving the solution of the problem of finding the global extremum of a function of one variable. For which a modified genetic algorithm is used, where the operators of the "generalized crossover" are stochastic matrices

A swarm optimization approach for solving workspace determination of parallel manipulators

Robotica ◽  
2014 ◽  
Vol 33 (3) ◽  
pp. 649-668 ◽  
Author(s):  
V. B. Saputra ◽  
S. K. Ong ◽  
A. Y. C. Nee
Keyword(s):  
Parallel Manipulator ◽  
Firefly Algorithm ◽  
Fitness Function ◽  
Computational Cost ◽  
Parallel Manipulators ◽  
Search Space ◽  
Optimization Approach ◽  
Physical Constraints ◽  
Interval Analysis Method ◽  
Two Stages

SUMMARYThis paper presents a novel method to determine the workspace of parallel manipulators using a variant of the Firefly Algorithm, which is one of the emerging techniques in swarm artificial intelligence. The workspace is defined as a set of all the coordinates in the search space that are accessible by the parallel manipulator end effector. The workspace formulation of the parallel manipulator considered in this paper has actuated and passive joint displacements which values are limited by their physical constraints. A special fitness function that discriminates between accessible and inaccessible coordinates is formulated based on the joint limitations. By finding these coordinates using the proposed Firefly Algorithm, the workspace of the manipulator can be constructed. The proposed method is an easy-to-implement alternative solution to the current numerical methods for workspace determination. The method consists of two stages of operation. The first stage maps the workspace to find the initial results with a space filling approach, in which a number of coordinates in the workspace are identified. The second stage refines the results with a boundary detection approach which focuses on the mapping of the boundaries of the workspace. The method is illustrated by its application to determine the 2D, 3D, and 6D workspaces of a Gough--Stewart Platform manipulator. Furthermore, the method is compared with a more rigorous interval analysis method in terms of computational cost and accuracy.

True Color Image Segmentation Using Quantum-Induced Modified-Genetic-Algorithm-Based FCM Algorithm

2021 ◽  
pp. 164-196
Author(s):  
Sunanda Das ◽  
Sourav De ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Execution Time ◽  
Color Image ◽  
Computational Cost ◽  
Cost Effective ◽  
Color Image Segmentation ◽  
Modified Genetic Algorithm ◽  
True Color Image ◽  
True Color

In this chapter, a quantum-induced modified-genetic-algorithm-based FCM clustering approach is proposed for true color image segmentation. This approach brings down the early convergence problem of FCM to local minima point, increases efficacy of conventional genetic algorithm, and decreases the computational cost and execution time. Effectiveness of genetic algorithm is tumid by modifying some features in population initialization and crossover section. To speed up the execution time as well as make it cost effective and also to get more optimized class levels some quantum computing phenomena like qubit, superposition, entanglement, quantum rotation gate are induced to modified genetic algorithm. Class levels which are yield now fed to FCM as initial input class levels; thus, the ultimate segmented results are formed. Efficiency of proposed method are compared with classical modified-genetic-algorithm-based FCM and conventional FCM based on some standard statistical measures.

True Color Image Segmentation Using Quantum-Induced Modified-Genetic-Algorithm-Based FCM Algorithm

2018 ◽  
pp. 55-94
Author(s):  
Sunanda Das ◽  
Sourav De ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Execution Time ◽  
Color Image ◽  
Computational Cost ◽  
Cost Effective ◽  
Color Image Segmentation ◽  
Modified Genetic Algorithm ◽  
True Color Image ◽  
True Color

In this chapter, a quantum-induced modified-genetic-algorithm-based FCM clustering approach is proposed for true color image segmentation. This approach brings down the early convergence problem of FCM to local minima point, increases efficacy of conventional genetic algorithm, and decreases the computational cost and execution time. Effectiveness of genetic algorithm is tumid by modifying some features in population initialization and crossover section. To speed up the execution time as well as make it cost effective and also to get more optimized class levels some quantum computing phenomena like qubit, superposition, entanglement, quantum rotation gate are induced to modified genetic algorithm. Class levels which are yield now fed to FCM as initial input class levels; thus, the ultimate segmented results are formed. Efficiency of proposed method are compared with classical modified-genetic-algorithm-based FCM and conventional FCM based on some standard statistical measures.

Automated Initial-Population Generation for Genetic-Algorithm-Based Assembly Planning

2000 ◽  
Author(s):  
Gregory C. Smith ◽  
Shiang-Fong Chen
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Assembly Planning ◽  
Initial Population ◽  
Search Efficiency ◽  
Assembly Sequence ◽  
Assembly Sequences ◽  
Component Assembly ◽  
Product Assembly ◽  
Initial Assembly

Abstract Genetic algorithms show particular promise for automated assembly planning. As a result, several recent research reports present genetic-algorithm-based mechanical-product assembly planners. However, genetic-algorithm-based assembly planners require an initial assembly-sequence population, and search efficiency greatly depends upon input-population quality. State-of-the-art genetic-algorithm-based assembly planners use one of two techniques for generating an initial assembly-sequence population: use a user-supplied assembly-sequence set or use a randomly generated assembly-sequence set. Generating a user-supplied initial population requires a substantial amount of manpower. Using a randomly generated initial population reduces search efficiency. As a result, we propose an algorithm for automatically generating an initial assembly-sequence population. Our algorithm calculates component assembly complexity and uses both component assembly complexity and component connectivity to automatically generate a valid assembly-sequence population. Using automatically generated initial populations, we achieve search efficiencies comparable to search efficiencies achieved when using user-supplied initial assembly-sequence populations, while eliminating manpower required to generate user-supplied assembly sequences.

scholarly journals Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A Distributed Genetic Algorithm Approach with Hierarchical Fitness Evaluation

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3526 ◽  
Author(s):  
Zi-Jia Wang ◽  
Zhi-Hui Zhan ◽  
Jun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Fitness Function ◽  
Computational Cost ◽  
Wireless Sensor ◽  
Fitness Evaluation ◽  
Disjoint Sets ◽  
Distributed Genetic Algorithm

This paper proposed a distributed genetic algorithm (DGA) to solve the energy efficient coverage (EEC) problem in the wireless sensor networks (WSN). Due to the fact that the EEC problem is Non-deterministic Polynomial-Complete (NPC) and time-consuming, it is wise to use a nature-inspired meta-heuristic DGA approach to tackle this problem. The novelties and advantages in designing our approach and in modeling the EEC problems are as the following two aspects. Firstly, in the algorithm design, we realized DGA in the multi-processor distributed environment, where a set of processors run distributed to evaluate the fitness values in parallel to reduce the computational cost. Secondly, when we evaluate a chromosome, different from the traditional model of EEC problem in WSN that only calculates the number of disjoint sets, we proposed a hierarchical fitness evaluation and constructed a two-level fitness function to count the number of disjoint sets and the coverage performance of all the disjoint sets. Therefore, not only do we have the innovations in algorithm, but also have the contributions on the model of EEC problem in WSN. The experimental results show that our proposed DGA performs better than other state-of-the-art approaches in maximizing the number of disjoin sets.

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