On initial populations of a genetic algorithm for continuous optimization problems

Maaranen, Heikki; Miettinen, Kaisa; Penttinen, Antti

doi:10.1007/s10898-006-9056-6

Cited by 144 publications

(61 citation statements)

References 26 publications

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“…Although there are several alternatives such as quasi random sequences and the simple sequential inhibition process, in our case, the chromosome initialization was carried out by means of a Pseudo-random Number Generator Algorithm (PNGA). This is because it is particularly well suited to cases where the speed of convergence is high [76], which may happen with the specific type of spatial objects with which we work. In addition, it is fast and easy to use (a detailed description of this process is addressed in Section 5.1).…”

Section: Ga Classification Of Polygonal Shapesmentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

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A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to polygon matching (inter-element matching), is obtained by means of a genetic algorithm that allows the classifying of area features from two geographical databases. After this, we apply a point to point matching (intra-element matching) based on the comparison of changes in their turning functions. This study shows that genetic algorithms are suitable for matching polygon features even if these features are quite different. Our results show up to 40% of matched polygons with differences in geometrical attributes. With regards to point matching, the vertex from homologous polygons, the function and threshold values proposed in this paper show a useful method for obtaining precise vertex matching.

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Section: Ga Classification Of Polygonal Shapesmentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

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“…45 Running the GA several times with different initial solution configurations and increasing the population size are simple approaches to increasing the coverage of the search space, thus reducing the impact of this problem. 46 In addition to the population size, other parameters of the genetic algorithm…”

Section: Selectionmentioning

confidence: 99%

Naturally selecting solutions

2013

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For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the postgenomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

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“…Although the initial population seeding phase is executed only once, it has an important role to improve the GA performance [22,23]. While the others GA phases are repeated [24,25]. A various initialization techniques have been introduced since the emergence of GAs concepts.…”

Section: Introductionmentioning

confidence: 99%

“…Further, the populations in each recursive generation process depend on their previous populations and at the end on the initial population seeding [25]. Therefore, specifying the initial population in GA is very important to decrease the computation time and then to find the optimal/near-optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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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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On initial populations of a genetic algorithm for continuous optimization problems

Cited by 144 publications

References 26 publications

A Polygon and Point-Based Approach to Matching Geospatial Features

A Polygon and Point-Based Approach to Matching Geospatial Features

Naturally selecting solutions

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

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