Performance Optimization of GA Based Planar Mechanism Synthesis

Lugo-González, Esther; Hernández-Gómez, Luis Héctor; Ponce-Reynoso, R.; Velázquez-Sánchez, Alejandro Tonatiu; Urriolagoitia-Sosa, Guillermo; Merchán-Cruz, Emmanuel Alejandro; Ramírez-Gordillo, Javier

doi:10.1109/cerma.2010.26

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The coding can be done in a binary representation [8,11], based on the domain of each variable (Figure 4). Figure 4 Chromosome structure [7] 5. Solution of study cases By applying the genetic algorithm developed to solve the equation 6, results shown in the figure 5 were obtained.…”

Section: Genetic Algorithm Description [7]mentioning

confidence: 99%

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Prediction of crack growth direction by Strain Energy Sih's Theory on specimens SEN under tension-compression biaxial loading employing Genetic Algorithms

Rodríguez-Martínez¹,

Lugo-González²,

Urriolagoitia-Calderón³

et al. 2011

J. Phys.: Conf. Ser.

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Abstract. Crack growth direction has been studied in many ways. Particularly Sih's strain energy theory predicts that a fracture under a three-dimensional state of stress spreads in direction of the minimum strain energy density [1]. In this work a study for angle of fracture growth was made, considering a biaxial stress state at the crack tip on SEN specimens. The stress state applied on a tension-compression SEN specimen is biaxial one on crack tip, as it can observed in figure 1.A solution method proposed to obtain a mathematical model considering genetic algorithms, which have demonstrated great capacity for the solution of many engineering problems. From equation (1) a mathematical deduction to solve in terms of of this case was developed employing Genetic Algorithms, where is a crack propagation direction in plane x-y. Steel and aluminium mechanical properties to modelled specimens were employed, because they are two of materials but used in engineering design. Obtained results show stable zones of fracture propagation but only in a range of applied loading.

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Section: Genetic Algorithm Description [7]mentioning

confidence: 99%

“…The steps to make a genetic algorithm, as defined in [8], are shown in the diagram of Figure 3. [7] Initial Population is created randomly and it is encoded within the chromosome of an array with variable length. The coding can be done in a binary representation [8,11], based on the domain of each variable (Figure 4).…”

Section: Genetic Algorithm Description [7]mentioning

confidence: 99%

Prediction of crack growth direction by Strain Energy Sih's Theory on specimens SEN under tension-compression biaxial loading employing Genetic Algorithms

Rodríguez-Martínez¹,

Lugo-González²,

Urriolagoitia-Calderón³

et al. 2011

J. Phys.: Conf. Ser.

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Performance of Simple Genetic Algorithm Inserting Forced Inheritance Mechanism and Parameters Relaxation

Lugo-González¹,

Merchán-Cruz²,

Hernández-Gómez³

et al. 2012

Bio-Inspired Computational Algorithms and Their Applications

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Genetic Algorithms (GA) are powerful tools to solve large scale design optimization problems. The research interests in GA lie in both its theory and application. On one hand, various modifications have been made to allow them to solve problems faster, more accurately and more reliably. Genetic Algorithms are a search paradigm that applies principles of evolutionary biology (crossover, mutation, natural selection) in order to deal with intractable search spaces. The power and success of GA are mostly achieved by the diversity with the individuals of a population which evolve, in parallel, following the principle of the survival of the fittest. In general, the genetic algorithms resolve combinatorial optimization problems that in (Goldberg, 1989) are mentioned, this implies a large number of responses associated with an exponential growth in solutions potentially feasible according to the magnitude of the problem. In a standard GA the diversity of the individuals is obtained and maintained using the genetic operators crossover and mutation which allow the GA to find feasible solutions and avoid premature convergence to a local maximum (Holland, 1975). The performance of a genetic algorithm, like any global optimization algorithm, depends on the mechanism for balancing the two conflicting objectives, which are exploiting the best solutions found so far and at the same time exploring the search space for promising solutions. The power of genetic algorithms comes from their ability to combine both exploration and exploitation in an optimal way (Holland, 1975). However, although this optimal utilization may be theoretically true for a genetic algorithm, there are problems in practice. These arise because of Holland assumed that the population size is infinite, that the fitness function accurately reflects the suitability of a solution, and that the interactions between genes are very small (Beasley et al., 1993). The evolutionary algorithm proposed in this paper is composed by a classic genetic algorithms along with the forced inheritance mechanism proposed by (

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Performance Optimization of GA Based Planar Mechanism Synthesis

Cited by 2 publications

References 12 publications

Prediction of crack growth direction by Strain Energy Sih's Theory on specimens SEN under tension-compression biaxial loading employing Genetic Algorithms

Prediction of crack growth direction by Strain Energy Sih's Theory on specimens SEN under tension-compression biaxial loading employing Genetic Algorithms

Performance of Simple Genetic Algorithm Inserting Forced Inheritance Mechanism and Parameters Relaxation

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