Crossover Operators in Genetic Algorithms: A Review

Umbarkar, A. J.; Sheth, P. D.

doi:10.21917/ijsc.2015.0150

Cited by 176 publications

(45 citation statements)

References 23 publications

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“…In the uniform crossover operation, recombination is performed swapping genes in the parents to be included in the offspring by choosing a random real number between 0 and 1. For each element in the selected row of the offspring, the random real number decides uniformly whether the element should be selected from the first or second parent [33].…”

Section: ) Crossover and Mutation Operatormentioning

confidence: 99%

DaTask: A Decomposition-Based Deadline-Aware Task Assignment and Workers’ Path-Planning in Mobile Crowd-Sensing

Akter

Yoon

2020

IEEE Access

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Mobile crowd-sensing (MCS) has recently become a promising approach for massive data collection, which empowers common people to perform sensing tasks with their smart devices. In MCS, locations of tasks and workers are diverse, and workers need to visit different task venues to perform the tasks. The diversity of task and worker locations, tasks' location accessibility, and required sensor type make the task assignment problem highly challenging. In time-sensitive MCS applications, this task assignment problem becomes even more intractable because of the deadline and a lot of possible movement trajectories of the workers. In this paper, we introduce two types of workers and formulate the task assignment problem, which comprises an embedded structure. Furthermore, a decomposition technique is applied to decompose the original problem into a main problem (the assignment problem) and a set of sub-problems (traveling salesman problems). The assignment problem determines task-worker assignments, and the sub-problems determine trajectories of the workers. This decomposition allows using a simpler solution strategy. Then, a memetic genetic algorithm is proposed to address the assignment problem, while each sub-problem is solved using an asymmetric traveling salesman problem heuristic. Results from simulations verify that the proposed algorithm outperforms the baseline methods under various experimental settings. INDEX TERMS Asymmetric traveling salesman problem, mobile crowd-sensing, memetic genetic algorithm, participatory sensing, task allocation.

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Section: ) Crossover and Mutation Operatormentioning

confidence: 99%

DaTask: A Decomposition-Based Deadline-Aware Task Assignment and Workers’ Path-Planning in Mobile Crowd-Sensing

Akter

Yoon

2020

IEEE Access

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“…8, then L random numbers are obtained from a uniform probability distribution, while each value is between '0' and '1'. First, the P-values are calculated and, in addition, the operator checks each value and if it be less than the parameter (usually 0.5), then the gene is picked from the parent1, otherwise, it is chosen from the parent2 [68][69][70].…”

Section: Uniform Crossovermentioning

confidence: 99%

“…Flat Crossover also applies the random numbers to reproduce one child from two parents.This operator functions the same as the uniform crossover, but the random numbers should be a subset of having the minimum and maximum of the genes. Generally, flat crossovers are employed in real-coded GAs [68][69][70][71][72]. (1 ) , 1,...,…”

Section: Flat (Blx) or Discrete Crossovermentioning

confidence: 99%

A Survey of Genetic Programming and Its Applications

Ahvanooey¹,

Li²,

Wu³

et al. 2019

KSII TIIS

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Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively rewriting them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

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“…The global convergence and search space must be considered for selecting the crossover operators. the effect of crossover operators in GA is dependent on the application, as well as encoding [21]. Here, the two-point arithmetic crossover is used.…”

Section: Selection Crossover and Mutationmentioning

confidence: 99%

Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling

et al. 2018

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Abstract:Sampling inspection uses the sample characteristics to estimate that of the population, and it is an important method to describe the population, which has the features of low cost, strong applicability and high scientificity. This paper aims at the sampling inspection of the master's degree thesis to ensure their quality, which is commonly estimated by random sampling. Since there are disadvantages in random sampling, a hybrid algorithm combined with an improved genetic algorithm and a simulated annealing algorithm is proposed in this paper. Furthermore, a novel mutation strategy is introduced according to the specialty of Shanghai's thesis sampling to improve the efficiency of sampling inspection; the acceleration of convergence of the algorithm can also take advantage of this. The new algorithm features the traditional genetic algorithm, and it can obtain the global optimum in the optimization process and provide the fairest sampling plan under the constraint of multiple sampling indexes. The experimental results on the master's thesis dataset of Shanghai show that the proposed algorithm well meets the requirements of the sampling inspection in Shanghai with a lower time-complexity.

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Crossover Operators in Genetic Algorithms: A Review

Cited by 176 publications

References 23 publications

DaTask: A Decomposition-Based Deadline-Aware Task Assignment and Workers’ Path-Planning in Mobile Crowd-Sensing

DaTask: A Decomposition-Based Deadline-Aware Task Assignment and Workers’ Path-Planning in Mobile Crowd-Sensing

A Survey of Genetic Programming and Its Applications

Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling

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