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Random search and parallel genetic algorithm comparison Random search SPGA EPGA_1 EPGA_2
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In this paper we introduce an efficient implementation of asynchronously parallel genetic algorithm with adaptive genetic operators. The classic genetic algorithm paradigm is extended with parallelization on one hand and an adaptive operators method on the other. The parallelization of the algorithm is achieved through multithreading mechanism, a v...
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Citations
... The flowchart of a GA is shown in Fig. 2. Adaptive genetic algorithms are a subset of genetic algorithms. They provide substantial functionality improvements over the traditional GA implementations [19]. The configuration of genetic algorithms operators such as the probability of mutation and probability of crossover is a significant deterministic factor which affects the performance of a GA. ...
Recommender Systems (RSs) are termed as web-based applications that make use of filtering methods and several machine learning algorithms to suggest relevant user objects. It can be said that some techniques are usually adopted or trained to develop these systems that generate lists of suitable recommendations. Conventionally, RS uses a single rating approach to preference user recommendation over an item. Recently, multi-criteria technique has been identified as a new approach of recommending user items based on several attributes or features of user items. This new technique of item recommendation has been adopted to solve several recommendation problems compared to the single rating approach. Furthermore, the predictive performance of the multi-criteria technique when tested proves to be further efficient as compared to the traditional single ratings approach. This paper gives a comparative study between two models that are based on the features and architecture of fuzzy sets system and adaptive genetic algorithm. Genetic Algorithms (GAs) are robust and stochastic search techniques centered on natural selection and evaluation that are often applied when encountering optimization problems. Fuzzy logic (FL) on the other hand, is known for its wide application in diverse fields in science. This study aims to evaluate, analyze, and compare the predictive performance of both methods and present their results. The study has been accomplished using Yahoo! Movies dataset, and the results of the performance of each model have been presented in this paper. The results proved that both techniques have significantly enhanced the system’s accuracy.
... Genetic algorithm is one of the best adaptive algorithms which is a heuristic random search mechanism (i.e. evolutionary process) [10].These adaptive algorithms can be used in traditional networks and Software-Defined Networks for better resource utilization. The possibilities of using such adaptive algorithms in such modular networks has already been researched and discussed in [11]. ...
Resource management in Software-Defined Networking (SDN) is an important aspect in this brand new approach of networking. Implementing an efficient resource management with high Quality-of-Service (QoS) requirements like low delay or high bandwidth is a big challenge. Dynamic and adaptive solutions can be benefit for improving the performance of these networks. In this paper, we propose a novel approach of using genetic algorithm with SDN architecture and FNSS toolchain for better optimization of adaptive resource management with testbeds on datacenter topologies with video streams run on VLC media player to evaluate the performance of our solution. We explained the testbeds on the test video (delay and bandwidth) by implementing the adaptive solution using Genetic Algorithm (GA) for adaptively selecting the capacity according to link delay which then reduces the overall timeof packet delivery and increases the bandwidth. The final results shows an improvementin reducing the average delay values of 94.4% when using OVS controller and 90.1% when using Floodlight controller, and an improvement in bandwidth of 950 Mbps-1.82 Mbps to 37.075 Mbps-42.9 Mbps in OVS controller and 952 Mbps-1.7 Mbps to 22.97 Mbps-27.45 Mbps in Floodlight controller.
... Instead of genotype diversity, [Budin et al. 1998] consider the diversity in the fitness of solutions. The degree of population diversity is estimated using the fitness of the best solutions f (s * ), the worst fitness f min , and the average fitness of the population f calculated as ...
Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide range of problems. Their robustness, however, may be affected by several adjustable parameters, such as mutation rate, crossover rate, and population size. Algorithm parameters are usually problem-specific, and often have to be tuned not only to the problem but even the problem instance at hand to achieve ideal performance. In addition, research has shown that different parameter values may be optimal at different stages of the optimisation process. To address these issues, researchers have shifted their focus to adaptive parameter control, in which parameter values are adjusted during the optimisation process based on the performance of the algorithm. These methods redefine parameter values repeatedly based on implicit or explicit rules that decide how to make the best use of feedback from the optimisation algorithm.
In this survey, we systematically investigate the state of the art in adaptive parameter control. The approaches are classified using a new conceptual model that subdivides the process of adapting parameter values into four steps that are present explicitly or implicitly in all existing approaches that tune parameters dynamically during the optimisation process. The analysis reveals the major focus areas of adaptive parameter control research as well as gaps and potential directions for further development in this area.
... This implies the sequential nature of the algorithm and calls for some sort of synchronization mechanism. Such a mechanism may slow down the parallel algorithm and it also makes the implementation somewhat more complicated and less portable [4]. The elimination selection type avoids both mentioned problems: it does not generate any duplicates; it only eliminates a single population member. ...
A global parallel genetic algorithm (GPGA) with elimination tournament selection without duplicates is described in this paper. The parallel implementation of genetic algorithm with multiple threads without synchronization is thoroughly investigated. The problem which occurs when several threads select the same individual for elimination is recognized in such implementation. The probability of that event is analytically formulated. Considering that probability, we determine the total number of iterations the asynchronous GPGA should perform in order to achieve the same optimization effect as a serial GA or a synchronous GPGA.
We have long known that characterizing protein structures structure is key to understanding protein function. Computational approaches have largely addressed a narrow formulation of the problem, seeking to compute one native structure from an amino-acid sequence. Now AlphaFold2 is shown to be able to reveal a high-quality native structure for many proteins. However, researchers over the years have argued for broadening our view to account for the multiplicity of native structures. We now know that many protein molecules switch between different structures to regulate interactions with molecular partners in the cell. Elucidating such structures
de novo
is exceptionally difficult, as it requires exploration of possibly a very large structure space in search of competing, near-optimal structures. Here we report on a novel stochastic optimization method capable of revealing very different structures for a given protein from knowledge of its amino-acid sequence. The method leverages evolutionary search techniques and adapts its exploration of the search space to balance between exploration and exploitation in the presence of a computational budget. In addition to demonstrating the utility of this method for identifying multiple native structures, we additionally provide a benchmark dataset for researchers to continue work on this problem.
This paper introduces a novel PSO-GA based hybrid training algorithm with Adam Optimization and contrasts performance with the generic Gradient Descent based Backpropagation algorithm with Adam Optimization for training Artificial Neural Networks. We aim to overcome the shortcomings of the traditional algorithm, such as slower convergence rate and frequent convergence to local minima, by employing the characteristics of evolutionary algorithms. PSO has a property of faster convergence rate, which can be exploited to account for the slower pace of convergence of the traditional BP (which is due to low values of gradients). In contrast, the integration with GA complements the drawback of convergence to local minima as GA, possesses the capability of efficient global search. So by this integration of these algorithms, we propose our new hybrid algorithm for training ANNs. We compare both the algorithms for the application of medical diagnosis. Results display that the proposed hybrid training algorithm, significantly outperforms the traditional training algorithm, by enhancing the accuracies of the ANNs with an increase of 20% in the average testing accuracy and 0.7% increase in the best testing accuracy.
In the research of Web content-based image retrieval, how to reduce more of the image dimensions without losing the main features of the image is highlighted. Many features of dimensional reduction schemes are determined by the breaking of higher dimensional general covariance associated with the selection of a particular subset of coordinates. This paper starts with analysis of commonly used methods for the dimension reduction of Web images, followed by a new algorithm for nonlinear dimensionality reduction based on the HSV image features. The approach obtains intrinsic dimension estimation by similarity calculation of two images. Finally, some improvements were made on the Parallel Genetic Algorithm (APGA) by use of the image similarity function as the self-adaptive judgment function to improve the genetic operators, thus achieving a Web image dimensionality reduction and similarity retrieval. Experimental results illustrate the validity of the algorithm.
More than a decade after the first extensive overview on parameter control, we revisit the field and present a survey of the state-of-the-art. We briefly summarize the development of the field and discuss existing work related to each major parameter or component of an evolutionary algorithm. Based on this overview, we observe trends in the area, identify some (methodological) shortcomings, and give recommendations for future research.
Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. However, how to quickly retrieving images is a challenge because that the speed and efficiency of retrieving image from Internet image is most important. We used genetic algorithm to improve the method based on HSV color space, and optimized the computational workload. First, the paper introduces how to extract dominant color of an image based on HSV color space. Then, it describes how to use genetic algorithm to optimize the algorithm of extracting dominant color. In the end, genetic algorithm is be used for the similarity measure of images. The experiments and results, which based on Corel database, showed that this method has greatly improved the image retrieval in time and precision rates.
