A Comparative Study of Crossover Operators for Genetic Algorithms to Solve Travelling Salesman Problem

Hameed, Wafaa Mustafa; Kanbar, Asan Baker

doi:10.29121/granthaalayah.v5.i2.2017.1740

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Various types of crossover operators to TSP are available in [8] and [9]. After recombination, some of the new individuals are chosen to be mutated.…”

Section: Literature Reviewmentioning

confidence: 99%

Genetic Algorithm for Solving Coverage Issues in Undersea Mining

Intriago¹,

Ochoa²,

López³

et al. 2021

2021 International Conference on Engineering and Emerging Technologies (ICEET)

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To be cost-effective, robot-based undersea mining must comply several operational constraints. Among the main constraints are the time and energy required to extract the mineral from the seabed. It is also important to reduce the wear of the joints that connect the ship on the surface with the robot crawler that does the mining on the seabed, since this not only reduces operating costs, but also lengthens the useful life of these parts which increases system security. For this reason, the least amount of twisting in these pieces is preferable, so it is advisable to reduce the number of turns or changes of direction in the trajectory of the robot that extracts the mineral. In this article, we present an algorithm to optimize Coverage Path Planning using Genetic Algorithm to produce paths with longer segments, which can be used in underwater mining and reduce the effects the mentioned turning problem. The resulting paths have on average 55% less changes of directions in the trajectory than a GA with standard cost function. In addition, in tests made by placing small obstacles in a random way, 76% of useful paths were obtained and up to 59% of useful path when the obstacles were grouped into a single larger obstacle.

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“…Various types of crossover operators to TSP are available in [8] and [9]. After recombination, some of the new individuals are chosen to be mutated.…”

Section: Literature Reviewmentioning

confidence: 99%

Genetic Algorithm for Solving Coverage Issues in Undersea Mining

Intriago¹,

Ochoa²,

López³

et al. 2021

2021 International Conference on Engineering and Emerging Technologies (ICEET)

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A New Lagrangian Problem Crossover—A Systematic Review and Meta-Analysis of Crossover Standards

Aladdin

Rashid

2023

Systems

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The performance of most evolutionary metaheuristic algorithms relies on various operators. The crossover operator is a standard based on population-based algorithms, which is divided into two types: application-dependent and application-independent crossover operators. In the process of optimization, these standards always help to select the best-fit point. The high efficiency of crossover operators allows engineers to minimize errors in engineering application optimization while saving time and avoiding overpricing. There are two crucial objectives behind this paper; first, we provide an overview of the crossover standards classification that has been used by researchers for solving engineering operations and problem representation. This paper proposes a novel standard crossover based on the Lagrangian Dual Function (LDF) to enhance the formulation of the Lagrangian Problem Crossover (LPX). The LPX for 100 generations of different pairs parent chromosomes is compared to Simulated Binary Crossover (SBX) standards and Blended Crossover (BX) for real-coded crossovers. Three unimodal test functions with various random values show that LPX has better performance in most cases and comparative results in other cases. Moreover, the LPB algorithm is used to compare LPX with SBX, BX, and Qubit Crossover (Qubit-X) operators to demonstrate accuracy and performance during exploitation evaluations. Finally, the proposed crossover stand operator results are demonstrated, proved, and analyzed statistically by the Wilcoxon signed-rank sum test.

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Using Ga for Evolving Weights in Neural Networks

Hameed

Kanbar

2019

acs

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This article aims at studying the behavior of different types of crossover operators in the performance of Genetic Algorithm. We have also studied the effects of the parameters and variables (crossover probability (pc), mutation probability (pm), population size (pop-size) and number of generation (NG)) for controlling the algorithm. This research accumulated most of the types of crossover operators these types are implemented on evolving weights of Neural Network problem. The article investigates the role of crossover in GAs with respect to this problem, by using a comparative study between the iteration results obtained from changing the parameters values (crossover probability, mutation rate, population size and number of generation). From the experimental results, the best parameters values for the Evolving Weights of XOR-NN problem are NG = 1000, pop-size = 50, pm = 0.001, pc = 0.5 and the best operator is Line Recombination crossover.

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A Comparative Study of Crossover Operators for Genetic Algorithms to Solve Travelling Salesman Problem

Cited by 3 publications

References 7 publications

Genetic Algorithm for Solving Coverage Issues in Undersea Mining

Genetic Algorithm for Solving Coverage Issues in Undersea Mining

A New Lagrangian Problem Crossover—A Systematic Review and Meta-Analysis of Crossover Standards

Using Ga for Evolving Weights in Neural Networks

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