Genetic Operators Crossover Research Articles

Hybrid-EPC: an Emperor Penguins Colony algorithm with crossover and mutation operators and its application in community detection

The idea of hybrid algorithms is formed due to the functional and structural differences in optimization algorithms. The goal is to create hybrid algorithms that can combine the strengths of the optimization algorithms to perform better in solving different problems. The Emperor Penguins Colony (EPC) algorithm is a population-based and nature-inspired optimization algorithm. This algorithm is powerful in finding global optima. In this paper, the standard EPC is improved by combining with genetic operators to finding better global optima. The genetic crossover and mutation operators have been used for modifying the decision vectors. These operators can cause a balance between exploration and exploitation. The balance between exploration and exploitation is effective in achieving a better optimal solution. The proposed algorithm called Hybrid-EPC is compared with GA, PSO, standard EPC, and Hybrid-PSO and tested on 20 various benchmark test functions. Also as an application, the proposed Hybrid-EPC algorithm is used for community detection in complex networks. For this purpose, the algorithm is tested on four social datasets and is compared with other community detection algorithms. The results show that this hybridization improves the standard EPC algorithm and has been successful in community detection.

Features of genetic algorithm for plain text encryption

The data communication has been growing in present day. Therefore, the data encryption became very essential in secured data transmission and storage and protecting data contents from intruder and unauthorized persons. In this paper, a fast technique for text encryption depending on genetic algorithm is presented. The encryption approach is achieved by the genetic operators Crossover and mutation. The encryption proposal technique based on dividing the plain text characters into pairs, and applying the crossover operation between them, followed by the mutation operation to get the encrypted text. The experimental results show that the proposal provides an important improvement in encryption rate with comparatively high-speed Processing.

Hybrid Fuzzy-Genetic Model for Fitness-Based Performance Optimization in Wireless Networks

In recent times, the application of autonomic soft computing techniques for design and optimization of wireless access networks is progressively becoming prevalent. These computational learning techniques are capable of handling uncertain and imprecise networking information while driving toward the optimal solution set in the problem search space. The approach proposed by this paper presents the application of the fuzzy logic inference combined with the evolutionary genetic algorithm to optimize the performance parameters in wireless networks. In particular, we consider optimal bit rate allocation and transmission power consumption through the joint design of fuzzy-genetic modeling framework. The sample training data generated through simulations of IEEE 802.11 wireless access network are analyzed for optimization by supplying it to the expert hybrid model comprising of the conjunctive design of both the computational intelligent techniques. Specifically, we contemplate the binary encoding scheme, single-point crossover, reversing mutation, and two fitness functions for executing the binary genetic operations of crossover and mutation. It is generally observed that the proposed hybrid model with polynomial fitness function yields better performance with scalable network datasets than the logarithmic fitness function in terms of higher objective value. Moreover, the results obtained through simulation experiments exhibit significant throughput gains and power efficiency for the deployed fitness functions with the evolving size of training dataset. Compared with the existing methods, our hybrid learning model demonstrates performance enhancement with higher expected fitness measure, improved throughput and power efficiency.

Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system

Recommender systems (RSs) have gained immense popularity due to their capability of dealing with a huge amount of information available in various domains. They are considered to be information filtering systems that make predictions or recommendations to users based on their interests. One of the most common recommender system techniques is user-based collaborative filtering. In this paper, we follow this technique by proposing a new algorithm which is called hybrid crow search and uniform crossover algorithm (HCSUC) to find a set of feasible clusters of similar users to enhance the recommendation process. Invoking the genetic uniform crossover operator in the standard crow search algorithm can increase the diversity of the search and help the algorithm to escape from trapping in local minima. The top-N recommendations are presented for the corresponding user according to the most feasible cluster’s members. The performance of the HCSUC algorithm is evaluated using the Jester dataset. A set of experiments have been conducted to validate the solution quality and accuracy of the HCSUC algorithm against the standard particle swarm optimization (PSO), African buffalo optimization (ABO), and the crow search algorithm (CSA). In addition, the proposed algorithm and the other meta-heuristic algorithms are compared against the collaborative filtering recommendation technique (CF). The results indicate that the HCSUC algorithm has obtained superior results in terms of mean absolute error, root means square errors and in minimization of the objective function.

Study of optimal allocation of water resources in Dujiangyan irrigation district of China based on an improved genetic algorithm

Abstract In order to distribute water resources reasonably, it is advantageous to make full use of resources and produce high economic and social benefits. Taking the Dujiangyan irrigation area of China as an example, we discuss the idea of establishing and solving the optimal allocation model of water resources. With this aim, a two-dimensional constraint model with highest economic value, minimum water shortage, minimum underground water consumption and necessary living water demand was established. In order to solve this model, we improved a multi-population genetic algorithm, extending the genetic optimization of the algorithm into two dimensions, taking population as the vertical dimension and the individual as the horizontal dimension, and transforming the cross genetic operator to copy the genetic (crossover) operator and the mutation operator to only act on the vertical dimension, so as to optimize the allocation of such discrete objectives of water resources in the irrigation area with a particular model suitable for the region. The distribution results successfully control the water shortage rate of each area at a low level, which saves the exploitation of groundwater to the maximum extent and produces high economic benefits. The improved algorithm proposed in this paper has a kind of strong optimization ability and provides a new solution for the optimization problem with multiple constraints.

Distributed corporate information systems optimization based on genetic algorithm and object model

In paper object model of distributed corporate information system is developed in the form of interacting models of its typical components, which takes into account replication and distribution of data between nodes of computer network, characteristics of hardware and makes it possible to simulating DCIS of any structure. A new modification of the genetic algorithm for the DCIS optimization problem has been developed, in which combined multichromosomes have been used to represent the placement of database tables, applications and parameters of servers, network devices and communication channels, genetic operators of recombination, crossover and mutation have been developed. To optimize the DCIS functioning, together with a modified genetic algorithm, the object model is used, that calculates the fitness-function, forming the optimal configuration of DCIS data distribution and hardware parameters in real time for the performance criterion formulated as the minimization of the system response time. A serial scheme of implementation of the genetic algorithm was developed, which includes full or partial optimization of DCIS in accordance with the requirements. The approach proposed allows obtaining suboptimal solution to the DCIS optimization problem with deviation from the global optimum of no more than 5%.

Improved Single Feistel Circuit Supporter by A Chaotic Genetic Operator

This document outlines a new color image encryption technology development. After splitting the original image into 240-bit blocks and modifying the first block by an initialization vector, an improved Feistel circuit is applied, sponsored by a genetic crossover operator and then strong chaining between the encrypted block and the next clear block is attached to set up the confusion-diffusion and heighten the avalanche effect, which protects the system from any known attack. Simulations carried out on a large database of color images of different sizes and formats prove the robustness of such a system.

Discrete Size and Shape Optimization of Truss Structures Based on Job Search Inspired Strategy and Genetic Operations

A meta-heuristic algorithm for discrete size and shape optimization of trusses via a job search inspired strategy together with genetic operators of mutation, selection, and crossover is proposed. The alternation of movements with respect to objective function and load bearing capacity of constructive decisions is provided. Being introduced is an intermediate search goal connected in terms of posed limitations with heightened suitability levels of individuals meeting the current requirements for the initial objective function. As soon as these conditions allow achieving a structure type which meets task limitations, requirements for the function value are redefined. This technique does not demand penalty functions that provide strict control of limitations in any algorithm usage, greater stability of the results received, and finding better solutions. The efficiency of this approach in terms of solution accuracy is demonstrated through five benchmark design examples, in comparison with other methods of discrete truss structure optimization.

Segmenting video frame images using genetic algorithms

Image segmentation plays an important role in computer vision. It is a process that partitions a digital image into several meaningful regions ,by identifying regions of an image that have common properties while separating regions that are dissimilar. The image segmentation problem is posed as an optimization procedure. In this thesis, an optimization approach based on genetic algorithm is introduced for finding optimal image segmentation. The design and implementation of genetic algorithm image segment or (GSAI) system are described. GSAI system employs finds optimal value using genetic operators crossover operator and mutation operator. The different proposed / implementation segmentation methods of the GSAI system were tested using Gray image are taken from one films and with size 352x240 pixels for video frames images of In this is work focused on genetic algorithm coefficients which affect in direct and active way in the work of GA to study and analysis dependable video images which are taken from video clips after partitioning to multiple frames.

Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search method is computationally expensive for selecting the optimal thresholds. Therefore, a hybrid bat algorithm with genetic crossover operation and smart inertia weight (SGA-BA) is proposed to choose the optimal thresholds. Furthermore, between-class variance (the Otsu method) and Kapur’s entropy are used as objective functions. In the novel SGA-BA, the smart inertia weight balances the SGA-BA’s exploration and exploitation based on the number of iterations and fitness values. Moreover, the local search capability of SGA-BA is strengthened by the crossover operation of the genetic algorithm. Meanwhile, the random vector is replaced by the beta distribution, which updates the frequency of bat in a smart way. The proposed SGA-BA was evaluated by a set of benchmark images with various levels of thresholds. Additionally, SGA-BA was compared with some well-known and recent heuristic algorithms, such as the genetic algorithm (GA), gravitational search algorithm (GSA), particle swarm optimization (PSO), whale optimization algorithm (WOA), improved salp swarm algorithm (LSSA) and basic bat algorithm (BA). The experimental results show that the proposed SGA-BA provides better outcomes than the other algorithms.

Research on Assembly Sequence Planning of Marine Diesel Engine Based on Improved Genetic Algorithm

That genetic algorithm is applied to the assembly sequence planning of marine diesel engine can improve assembly planning efficiency a lot. The genetic algorithm crossover operator of genetic algorithm is improved. By combining the clashes matrix and priority relationship matrix, the relational-clashes matrix is established. In order to get the assembly sequence which can assembly safely and effectively, but conform to the actual assembly habits as well, the fitness function combines the feasibility of assembly sequence, the rationality of assembly sequence and the number of redirection. The evaluation method based on the general principle of assembly sequence was proposed for the first time. Priority of parts is evaluated according to their barycentric coordinates and precision level. The evaluation method can enrich the evaluation method of assembly sequences and expand the application of intelligent algorithms in the field. After some tests and analysis of the experimental results, the algorithm proved to be convergent and the results are effective.

Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network

BackgroundMining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.ResultsWe compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets.ConclusionsThe presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.

Latent Feature Group Learning for High-Dimensional Data Clustering

In this paper, we propose a latent feature group learning (LFGL) algorithm to discover the feature grouping structures and subspace clusters for high-dimensional data. The feature grouping structures, which are learned in an analytical way, can enhance the accuracy and efficiency of high-dimensional data clustering. In LFGL algorithm, the Darwinian evolutionary process is used to explore the optimal feature grouping structures, which are coded as chromosomes in the genetic algorithm. The feature grouping weighting k-means algorithm is used as the fitness function to evaluate the chromosomes or feature grouping structures in each generation of evolution. To better handle the diverse densities of clusters in high-dimensional data, the original feature grouping weighting k-means is revised with the mass-based dissimilarity measure rather than the Euclidean distance measure and the feature weights are optimized as a nonnegative matrix factorization problem under the orthogonal constraint of feature weight matrix. The genetic operations of mutation and crossover are used to generate the new chromosomes for next generation. In comparison with the well-known clustering algorithms, LFGL algorithm produced encouraging experimental results on real world datasets, which demonstrated the better performance of LFGL when clustering high-dimensional data.

Using improved firefly algorithm based on genetic algorithm crossover operator for solving optimization problems

Using improved firefly algorithm based on genetic algorithm crossover operator for solving optimization problems

A genetic algorithm for spatiosocial tensor clustering

Tensor clustering is a knowledge management technique which is well known as a major algorithmic and technological driver behind a broad applications spectrum. The latter ranges from multimodal social media analysis and geolocation processing to analytics tailored for large omic data. However, known exact tensor clustering problems when reduced to tensor factorization are provably NP hard. This is attributed in part to the volume of data contained in a tensor, proportional to the product of its dimensions, as well as to the increased interdependency between the tensor entries across its dimensions. One well studied way to circumvent this inherent difficulty is to resort to heuristics. This article presents an enhanced version of a genetic algorithm tailored for community discovery structure in tensors containing spatiosocial data, namely linguistic and geolocation data. The objective function as well as the chromosome fitness functions by design take into account elements of linguistic propagation models. The genetic operators of selection, crossover, and mutation as well as the newly added double mutation operator work directly on the community level. Moreover, various policies for maintaining gene variability across generations are studied in an extensive simulation powered by Google TensorFlow. As with its predecessor, the proposed genetic algorithm has been applied to a dataset consisting of a large number of Tweets and their associated geolocations from the Grand Duchy of Luxembourg, a historically and de facto trilingual country. The results are compared with those obtained from the original genetic algorithm and their differences are interpreted.

A Genetic Algorithm for Stochastic Inversion in Contaminant Subsurface Hydrology.

Identifying the spatial distribution of hydrological properties of aquifers is a key problem in subsurface hydrology. The aquifer structure plays an important role in contaminant transport. Identifying the properties (primarily the hydraulic conductivity) is essentially an inversion problem that is ill-posed, non-unique and computationally intensive by definition. In this work, the non-uniqueness of the inverse problem is tackled via a novel Genetic Algorithm approach combined with a geostatistical method (Sequential Indicator Simulations) for construction of realizations of properties spatial distributions, which are modeled as random. The Genetic Algorithm cross-over operator is based on a novel concept of pilot-planes: daughter realizations adopt pilot-planes from one of their parents. In addition, each aquifer realization is conditioned on the geological hard data and is constructed by sampling the facies distribution, evaluated by indicator variograms. The approach is illustrated in two test cases: a synthetic two-dimensional (2D) case and an actual three-dimensional (3D) case. The results have shown the ability of the proposed approach to generate a set of realizations, where each individual exhibits minor deviations from the measurements. Further, a comparison between the proposed approach and direct (Monte Carlo) approach shows that the Genetic Algorithm was able to generate an ensemble of solutions with a better fitting of the measurements than the direct approach by a significantly reduced computational effort.

Genetic optimization mapping applied to medical image segmentation

A number of important optimization problems have been classified as mapping applied towards segmentation of important features. The segmentation of important features can be formulated as configurational mapping problems by representing mapping configurations as solutions to problems of interest. One example of such configuration mapping is found in image segmentation where an image can be represented as unique subsets of a complete image and then evolved through mapping to become a segment of specific interest within an image. An effective segmentation mapping algorithm must determine the specific image subsets of an image field that best exhibit an a priori set of quantitative and qualitative characteristics. In this paper, a Genetic Optimization Mapping Algorithm is used to produce a population of sub-images, characteristic of specific image subsets of interest that were tested via a quantitative objective function, ranked using a linear fitness scheme, and modified using a genetic Crossover operator. The mapping algorithm is found to converge, within fifty to one hundred generations of maps, to a good fit to the targeted mapping configuration in a very robust and efficient manner.

An Efficient Adaptive Genetic Algorithm (GA) Technique to Improve the Neural Network (NN) Performance with aid of Adaptive GA Operators

The neural network (NN) performance improvement is one of the major topics. Thus an adaptive genetic algorithm (AGA) technique is proposed by making adaptive with respect to genetic operators like crossover and mutation. Our adaptive GA technique starts with the generation of initial population as same as the normal GA and performs the fitness calculation for each individual generated chromosome. After that, the genetic operator's crossover and mutation will be performed on the best chromosomes. The AGA technique will be utilised in the NN performance improvement process. The AGA will utilise some parameters obtained from the NN by back propagation algorithm. The utilisation of NN parameters by AGA will improve the NN performance. Hence, the NN performance can be improved more effectively by achieving high performance ratio than the conventional GA with NN. The technique will be implemented in the working platform of MATLAB and the results will be analysed to demonstrate the performance of the proposed AGA.

The Crossover Operator of a Genetic Algorithm as Applied to the Task of a Production Planning

In this paper we propose using a genetic crossover operator for the task of detecting the optimal queue of a product parts. We performed a literature review on the problem and defined the goal of this work. An offered crossover operator is presented in a formalized way. Based on this operator was developed the optimizing software. Comparative analysis of the developed software and the existed solution was performed.

Trajectory Optimization of Pneumatic Marking Needle Based on Genetic Algorithm

Due to the ability to print all types of characters and patterns on the surface of metal parts or components, pneumatic marking machine is widely used in machinery, automobile, aerospace and other fields. For reducing equipment wear and extending equipment life, reducing the printing time as well as printing distance are the key points. In this paper, path optimization objective function model is established which refers to the TSP model. And the genetic operations such as genetic coding, fitness function, selection operator, crossover operator and mutation operator are described. The simulation results show that the optimization of the pneumatic marking path by using genetic algorithm can significantly improve the marking efficiency.