Comparative review of selection techniques in genetic algorithm

Shukla, Anupriya; Pandey, Hari Mohan; Mehrotra, Deepti

doi:10.1109/ablaze.2015.7154916

Cited by 163 publications

(79 citation statements)

References 11 publications

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“…One may consider to extend the parameter tuning procedure by applying more sophisticated methods such as GAGA approach using a metaGA above the main algorithm to tune the parameters [5]. Also, different selection operators may be tested to evaluate the influence of a chosen operator on the results [24]. Although tournament selection is described in the literature as a well-performing operator, some other operators may be more accurate for particular problems than the commonly recommended ones.…”

Section: Discussionmentioning

confidence: 99%

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Designing Distributed Cell Classifier Circuits using a Genetic Algorithm

Nowicka

Siebert

2019

Preprint

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Cell classifiers are decision-making synthetic circuits that allow in vivo cell-type classification. Their design is based on finding a relationship between differential expression of miRNAs and the cell condition. Such biological devices have shown potential to become a valuable tool in cancer treatment as a new type-specific cell targeting approach. So far, only single-circuit classifiers were designed in this context. However, reliable designs come with high complexity, making them difficult to assemble in the lab. Here, we apply so-called Distributed Classifiers (DC) consisting of simple single circuits, that decide collectively according to a threshold function. Such architecture potentially simplifies the assembly process and provides design flexibility. Here, we present a genetic algorithm that allows the design and optimization of DCs. Breast cancer case studies show that DCs perform with high accuracy on realworld data. Optimized classifiers capture biologically relevant miRNAs that are cancer-type specific. The comparison to a single-circuit classifier design approach shows that DCs perform with significantly higher accuracy than individual circuits. The algorithm is implemented as an open source tool.

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Section: Discussionmentioning

confidence: 99%

“…Many selection operators are described in the literature. Tournament selection allows to maintain diversity in the population and can be efficiently implemented [24]. In each selection iteration two parents are chosen in separate tournaments.…”

Section: Operatorsmentioning

confidence: 99%

Designing Distributed Cell Classifier Circuits using a Genetic Algorithm

Nowicka

Siebert

2019

Preprint

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“…GA can be used in these types of optimization problems. These algorithms are designed to mimic the Darwin's fittest principle of survival, which is based on the best individuals having a better chance of adapting themselves to a specific environment and surviving…”

Section: Proposed Methodologymentioning

confidence: 99%

“…GA can be used in these types of optimization problems. These algorithms are designed to mimic the Darwin's fittest principle of survival, 31 which is based on the best individuals having a better chance of adapting themselves to a specific environment and surviving. 32 The operation of a GA consists of the inclusion of a group of indi- corresponding to the distance, in which the trajectories cannot have a length greater than 90 mm.…”

Section: Proposed Trajectories Selection Algorithmmentioning

confidence: 99%

Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms

Villanueva-Naquid

Soubervielle‐Montalvo

Aguilar-Ponce

et al. 2020

Robotics Computer Surgery

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Background: Preoperative assessment to find the safest trajectory in keyhole neurosurgery can reduce post operative complications. Methods: We introduced a novel preoperative risk assessment semiautomated methodology based on the sum of N maximum risk values using a generic genetic algorithm for the safest trajectory search. Results: A set of candidates trajectories were found for two surgical procedures. The trajectories search is done using a risk map considering the proximity of voxels within risk structures in multiple points and a genetic algorithm to avoid an exhaustive search. The trajectories were validated by a group of neurosurgeons. Conclusions: The trajectories obtained with the proposal method were shorter in 5% and have greater distance from the voxels within the blood vessels in 4.7%. The use of genetic algorithm (GA) speeds up the search for the safest trajectory, decreasing in 99.9% the time required for an exhaustive search.

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“…Thus, there is a chance to produce offspring with higher fitness. A various crossover operators can be applied with GAs [40,41]. Let us begins with single-point crossover and two-point crossover, then continue the process using another technique to fit some situations, see Figure 3.…”

Section: Basic Principles Of Genetic Algorithmsmentioning

confidence: 99%

An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques

et al. 2018

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Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary computation that plays a significant role in obtaining meaningful solutions to complex problems with large search space. GAs involve three fundamental operations after creating an initial population, namely selection, crossover, and mutation. The first task in GAs is to create an appropriate initial population. Traditionally GAs with randomly selected population is widely used as it is simple and efficient; however, the generated population may contain poor fitness. Low quality or poor fitness of individuals may lead to take long time to converge to an optimal (or near-optimal) solution. Therefore, the fitness or quality of initial population of individuals plays a significant role in determining an optimal or near-optimal solution. In this work, we propose a new method for the initial population seeding based on linear regression analysis of the problem tackled by the GA; in this paper, the traveling salesman problem (TSP). The proposed Regression-based technique divides a given large scale TSP problem into smaller sub-problems. This is done using the regression line and its perpendicular line, which allow for clustering the cities into four sub-problems repeatedly, the location of each city determines which category/cluster the city belongs to, the algorithm works repeatedly until the size of the subproblem becomes very small, four cities or less for instance, these cities are more likely neighboring each other, so connecting them to each other creates a somehow good solution to start with, this solution is mutated several times to form the initial population. We analyze the performance of the GA when using traditional population seeding techniques, such as the random and nearest neighbors, along with the proposed regression-based technique. The experiments are carried out using some of the well-known TSP instances obtained from the TSPLIB, which is the standard library for TSP problems. Quantitative analysis is carried out using the statistical test tools: analysis of variance (ANOVA), Duncan multiple range test (DMRT), and least significant difference (LSD). The experimental results show that the performance of the GA that uses the proposed regression-based technique for population seeding outperforms other GAs that uses traditional population seeding techniques such as the random and the nearest neighbor based techniques in terms of error rate, and average convergence.

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Comparative review of selection techniques in genetic algorithm

Cited by 163 publications

References 11 publications

Designing Distributed Cell Classifier Circuits using a Genetic Algorithm

Designing Distributed Cell Classifier Circuits using a Genetic Algorithm

Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms

An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques

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