2019
DOI: 10.1007/978-3-030-33103-0_15
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Genetic Algorithm for Shortest Path in Ad Hoc Networks

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Cited by 9 publications
(9 citation statements)
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“…The genetic algorithm is an evolutionary metaheuristic. It is an iterative algorithm (Figure 4) operating on populations of individuals compounds of chromosome vectors represented in integer, binary or real code [2]. These individuals will evolve from one generation to another to constitute a set of solutions for the problem.…”
Section: Figure 3: Mutation Operatormentioning
confidence: 99%
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“…The genetic algorithm is an evolutionary metaheuristic. It is an iterative algorithm (Figure 4) operating on populations of individuals compounds of chromosome vectors represented in integer, binary or real code [2]. These individuals will evolve from one generation to another to constitute a set of solutions for the problem.…”
Section: Figure 3: Mutation Operatormentioning
confidence: 99%
“…It has proved successful algorithms for solving problems of optimization of composite structures. Evolutionary algorithms have proved their adaptations to the constraints of problems and have been able to overcome local minima problems such as GA (Genetic Algorithm) [2], ACO (Ant Colony Optimization) [3] and TS (Tabu search) [4].…”
Section: Introductionmentioning
confidence: 99%
“…When conducting a simulation experiment, the minimum operating time of GA with the CPU-architecture processing 5,000 chromosomes was 1,397 ms, and the maximum was 18,311 ms: but, at the same time, in other cases, the running time of the algorithm did not exceed 3,100 ms. Therefore, let's consider the distribution of the duration of the algorithm in ms on the interval [1,200,3,200] in more detail.…”
Section: The Research Issuementioning
confidence: 99%
“…According to the data presented in Fig. 5, it can be argued that the set G * contains two spaces [1,400,1,599] and [1,600,1,799].…”
Section: The Research Issuementioning
confidence: 99%
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