Mathematical analysis of schema survival for genetic algorithms having dual mutation

Mishra, Apoorva; Shukla, Anupam

doi:10.1007/s00500-017-2650-3

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…Corus e Oliveto [11] realizaram experimentos com relac ¸ão ao número de indivíduos na populac ¸ão e demostraram como a interac ¸ão entre os operadores de mutac ¸ão e cruzamento beneficia mais o AG do que fazer apenas a mutac ¸ão, utilizando para isso cadeias de Markov. Mishra e Shukla [43] trazem um teorema matemático que representa a probabilidade de sobrevivência de um esquema após a aplicac ¸ão do cruzamento e da mutac ¸ão e que resultou em uma mutac ¸ão de duas fases que melhora o poder exploratório do AG.…”

Section: Principais Críticas E Avanc ¸Osunclassified

“…A formulac ¸ão de AGs paralelos e metodologias para projetar mecanismos adaptativos e verificac ¸ão de desempenho para novas classes populacionais podem ser consultadas em [1], [37], [47] [24], [29], [53], [50], [77] [6], [51], [34]. Abordagens mais generalistas que incluem uma série de diferentes combinac ¸ões entre os elementos do AG foram propostas por [6,15,41,43,50,55,66]. A mais recente revisão sistemática da literatura sobre os recentes avanc ¸os em AG é apresentada por [28].…”

Section: Direc ¸õEs Futuras Para a Melhoria Do Algoritmo Genéticounclassified

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Algoritmo Genético: Principais Gaps, Trade-offs e Perspectivas para Futuras Pesquisas

Pinto

Martarelli

Nagano

2022

TCAM

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Recebido em 30 de janeiro de 2021 / Aceito em 1 de fevereiro de 2022 RESUMO. O Algoritmo Genético (AG) é caracterizado por ser uma meta-heurística mimetizada no processo genético de evoluc ¸ão natural baseada na Teoria dos Esquemas (TE) e pela Hipótese dos Blocos Construtivos (HBC). É fundamentado na busca por boas soluc ¸ões mediante a ac ¸ão de operadores genéticos que, se configurados indevidamente, podem inviabilizar a otimizac ¸ão devido ao funcionamento inadequado da TE e da HBC. As dificuldades em projetar designs de alta aptidão e as insuficientes provas teóricas sobre a TE e a HBC retratam o dilema fundamental do AG. Portanto, este artigo tem como objetivo prover uma melhor compreensão dos efeitos que a ac ¸ão dos operadores genéticos exerce sobre a TE e a HBC. A partir de uma revisão tradicional da literatura, que explora o arcabouc ¸o teórico da TE e da HBC, apresentamos importantes reflexões sobre os principais gaps, trade-offs e perspectivas futuras sobre o AG.

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Section: Principais Críticas E Avanc ¸Osunclassified

Section: Direc ¸õEs Futuras Para a Melhoria Do Algoritmo Genéticounclassified

Algoritmo Genético: Principais Gaps, Trade-offs e Perspectivas para Futuras Pesquisas

Pinto

Martarelli

Nagano

2022

TCAM

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“…Customers and routes are compared to arcs and nodes of an extension graph. Certain problem‐solving techniques are presented by Mishra et al, 9 including the FCFS, segmentation, and insertion techniques. The initiation of meta‐heuristic approaches like Tabu search, ACO, GA and PSO to resolve DVR problems was made possible by developing heuristic methodologies.…”

Section: Introductionmentioning

confidence: 99%

Modeling a novel heuristic model for handling vehicle routing using multiobjective optimization

Praveen¹,

Periasamy

Gurusamy³

et al. 2023

Concurrency and Computation

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SummaryElectric vehicles (EVs) are drastically growing, and their emergence is more popular. Moreover, with the constraint driving range, the batteries of these vehicles are rechargeable throughout the routes in various situations. Here, a new problem is formulated with the inclusion of both battery swapping and partial recharging decisions. The energy level and the routing directions are more important, and the investigators intend to handle these issues effectually. However, the major constraint is routing and time window. Here, a heuristic model (LTS‐RSS) is designed to find the best matching solution. A novel Random Sub‐Space (RSS) and local Tabu search (LTS) are modeled to handle these issues. The probabilistic model of RSS is anticipated by integrating the consequences of time windows and distances. The experimentation is done with an online database and used for performance validation. The outcomes show that the newly modeled (LTS‐RSS) approach enhances the significance of the model. The outcomes of all the instances with diverse strategies enhance the model's robustness and stability for resolving these issues. The empirical analysis is done with MATLAB 2020b simulator, and metrics like optimization of routing solution, the best vehicle, best distance, and numbers of vehicles are evaluated, and the outcomes are compared with various other approaches.

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“…The terminal resistance is the optimization parameter. There are series of optimization algorithms that have been put forward to solve this type of optimization problem, such as Genetic algorithm [9,10], Ant Colony Algorithm [11,12], Simulated Annealing Algorithm [13,14], Particle Swarm Optimization [15,16], etc. Particle Swarm Optimization is a spatial search algorithm that solves the optimization problem and is very suitable for solving multidimensional problems.…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimization for Terminal Resistor Matching

Nan¹,

Jiang²,

Xu³

2019

dteees

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The CAN bus is widely used in the industrial field and its terminal resistance has an important influence on the bus signal. In this paper, a particle swarm optimization based on the joint simulation of Saber and MATLAB is proposed to address this problem. First, we transform the terminal resistor matching problem into a minimisation multi-objective optimization problem and formulate the objective function by considering three objectives: bus voltage, rise time and overshoot. Then, we establish a network topology model of CAN bus in Saber, and combine saber and MATLAB. Finally, virtual simulation and physical verification are conducted and the results demonstrate that the optimized terminating resistor has a significant improvement on the signal quality of the bus, and algorithm has engineering applicability.

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Mathematical analysis of schema survival for genetic algorithms having dual mutation

Cited by 11 publications

References 14 publications

Algoritmo Genético: Principais Gaps, Trade-offs e Perspectivas para Futuras Pesquisas

Algoritmo Genético: Principais Gaps, Trade-offs e Perspectivas para Futuras Pesquisas

Modeling a novel heuristic model for handling vehicle routing using multiobjective optimization

A Particle Swarm Optimization for Terminal Resistor Matching

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