Genetic Algorithm: Reviews, Implementations, and Applications

Mutation Operators ◽

Hybrid Computation ◽

Engineering Pedagogy ◽

Crossover And Mutation ◽

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation

Genetic Algorithm: Reviews, Implementations, and Applications

10.36227/techrxiv.12657173 ◽

2020 ◽

Cited By ~ 1

Author(s):

Tanweer Alam ◽

Shamimul Qamar ◽

Amit Dixit ◽

Mohamed Benaida

Keyword(s):

Genetic Algorithm ◽

Natural Selection ◽

Behavioral Science ◽

Random Search ◽

Mutation Operators ◽

Hybrid Computation ◽

Engineering Pedagogy ◽

Crossover And Mutation ◽

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation

Genetic Algorithm: Reviews, Implementations, and Applications

International Journal of Engineering Pedagogy (iJEP) ◽

10.3991/ijep.v10i6.14567 ◽

2020 ◽

Vol 10 (6) ◽

pp. 57

Author(s):

Tanweer Alam ◽

Shamimul Qamar ◽

Amit Dixit ◽

Mohamed Benaida

Keyword(s):

Genetic Algorithm ◽

Natural Selection ◽

Behavioral Science ◽

Random Search ◽

Adaptive Technology ◽

Mutation Operators ◽

Engineering Pedagogy ◽

Crossover And Mutation ◽

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Genetic Algorithm Reviews, Implementations, and Applications

10.31219/osf.io/8tng5 ◽

2020 ◽

Author(s):

Tanweer Alam ◽

Shamimul Qamar ◽

Amit Dixit ◽

Mohamed Benaida

Keyword(s):

Genetic Algorithm ◽

Behavioral Science ◽

Random Search ◽

Mutation Operators ◽

Adaptive Technique ◽

Hybrid Computation ◽

Engineering Pedagogy ◽

Crossover And Mutation ◽

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively man-age the searching system strategy. This algorithm is derived from natural se-lection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the im-proved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random test-ing, historical information is provided with intelligent enslavement to contin-ue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm

Electronics ◽

10.3390/electronics9040687 ◽

2020 ◽

Vol 9 (4) ◽

pp. 687

Author(s):

Fang Ye ◽

Jie Chen ◽

Yuan Tian ◽

Tao Jiang

Keyword(s):

Genetic Algorithm ◽

Random Search ◽

Task Assignment ◽

Optimization Method ◽

Adaptive Genetic Algorithm ◽

Mutation Operators ◽

Fitness Value ◽

Coupling Constraints ◽

Crossover And Mutation ◽

And Task

The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven.

Simulation of the Pressure-Bearing Capacity Test Model in Architectural Decoration Design

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.716-717.391 ◽

2014 ◽

Vol 716-717 ◽

pp. 391-394

Author(s):

Li Mei Guo ◽

Ai Min Xiao

Keyword(s):

Genetic Algorithm ◽

Bearing Capacity ◽

Test Method ◽

Test Model ◽

Improved Genetic Algorithm ◽

Good Convergence ◽

Mutation Operators ◽

Process Pressure ◽

Calculation Process ◽

in architectural decoration process, pressure-bearing capacity test is the foundation of design, and is very important. To this end, a pressure-bearing capacity test method in architectural decoration design is proposed based on improved genetic algorithm. The selection, crossover and mutation operators in genetic algorithm are improved respectively. Using its fast convergence characteristics eliminate the pressure movement in the calculation process. The abnormal area of pressure-bearing existed in buildings which can ensure to be tested is added, to obtain accurate distribution information of the abnormal area of pressure-bearing. Simulation results show that the improved genetic algorithm has good convergence, can accurately test the pressure-bearing capacity in architectural decoration.

A Genetic Algorithm Based Procedure for Extracting Optimal Solutions From a Morphological Chart

Volume 6: 33rd Design Automation Conference, Parts A and B ◽

10.1115/detc2007-35497 ◽

2007 ◽

Cited By ~ 1

Author(s):

Santosh Tiwari ◽

Joshua Summers ◽

Georges Fadel

Keyword(s):

Genetic Algorithm ◽

Optimization Problem ◽

Mathematical Representation ◽

Optimal Solutions ◽

Proof Of Concept ◽

Optimization Framework ◽

Mutation Operators ◽

Novel Approach ◽

Morphological Chart ◽

A novel approach using a genetic algorithm is presented for extracting globally satisfycing (Pareto optimal) solutions from a morphological chart where the evaluation and combination of “means to sub-functions” is modeled as a combinatorial multi-objective optimization problem. A fast and robust genetic algorithm is developed to solve the resulting optimization problem. Customized crossover and mutation operators specifically tailored to solve the combinatorial optimization problem are discussed. A proof-of-concept simulation on a practical design problem is presented. The described genetic algorithm incorporates features to prevent redundant evaluation of identical solutions and a method for handling of the compatibility matrix (feasible/infeasible combinations) and addressing desirable/undesirable combinations. The proposed approach is limited by its reliance on the quantifiable metrics for evaluating the objectives and the existence of a mathematical representation of the combined solutions. The optimization framework is designed to be a scalable and flexible procedure which can be easily modified to accommodate a wide variety of design methods that are based on the morphological chart.

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

A Genetic Algorithm with Conditional Crossover and Mutation Operators and Its Application to Combinatorial Optimization Problems

10.1093/ietfec/e90-a.1.287 ◽

2007 ◽

Vol E90-A (1) ◽

pp. 287-294 ◽

Cited By ~ 1

Author(s):

R.-L. WANG ◽

S. FUKUTA ◽

J.-H. WANG ◽

K. OKAZAKI

Keyword(s):

Genetic Algorithm ◽

Combinatorial Optimization ◽

Optimization Problems ◽

Combinatorial Optimization Problems ◽

Mutation Operators ◽

Transmission Network Expansion Planning under Improved Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.347-353.1458 ◽

2011 ◽

Vol 347-353 ◽

pp. 1458-1461

Author(s):

Hong Fan ◽

Yi Xiong Jin

Keyword(s):

Genetic Algorithm ◽

Linear Optimization ◽

Large Integer ◽

Transmission Network ◽

Improved Genetic Algorithm ◽

Network Expansion ◽

Expansion Planning ◽

Mutation Operators ◽

Crossover And Mutation ◽

Search Capability

Improved genetic algorithm for solving the transmission network expansion planning is presented in the paper. The module which considered the investment costs of new transmission facilities. It is a large integer linear optimization problem. In this work we present improved genetic algorithm to find the solution of excellent quality. This method adopts integer parameter encoded style and has nonlinear crossover and mutation operators, owns strong global search capability. Tests are carried out using a Brazilian Southern System and the results show the good performance.

Adaptive Genetic Algorithm for Hybrid Flow-Shop Scheduling

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.753-755.2925 ◽

2013 ◽

Vol 753-755 ◽

pp. 2925-2929

Author(s):

Xiao Chun Zhu ◽

Jian Feng Zhao ◽

Mu Lan Wang

Keyword(s):

Genetic Algorithm ◽

Flow Shop ◽

Flow Shop Scheduling ◽

Hybrid Flow Shop ◽

Adaptive Genetic Algorithm ◽

Shop Scheduling ◽

Mutation Operators ◽

Hybrid Flow Shop Scheduling ◽

Encoding Method ◽

This paper studies the scheduling problem of Hybrid Flow Shop (HFS) under the objective of minimizing makespan. The corresponding scheduling simulation system is developed in details, which employed a new encoding method so as to guarantee the validity of chromosomes and the convenience of calculation. The corresponding crossover and mutation operators are proposed for optimum sequencing. The simulation results show that the adaptive Genetic Algorithm (GA) is an effective and efficient method for solving HFS Problems.