An Improved Genetic Algorithm and A New Discrete Cuckoo Algorithm for Solving the Classical Substitution Cipher

2021 ◽  
pp. 1431-1455
Author(s):  
Ashish Jain ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Problems ◽  
Secret Key ◽  
Improved Genetic Algorithm ◽  
Efficiency And Effectiveness ◽  
Effective Option ◽  
Np Complete ◽  
Metaheuristic Techniques ◽  
Better Than ◽  
Substitution Cipher

Searching secret key of classical ciphers in the keyspace is a challenging NP-complete problem that can be successfully solved using metaheuristic techniques. This article proposes two metaheuristic techniques: improved genetic algorithm (IGA) and a new discrete cuckoo search (CS) algorithm for solving a classical substitution cipher. The efficiency and effectiveness of the proposed techniques are compared to the existing tabu search (TS) and genetic algorithm (GA) techniques using three criteria: (a) average number of key elements correctly detected, (b) average number of keys examined before determining the required key, and (c) the mean performance time. As per the results obtained, the improved GA is comparatively better than the existing GA for criteria (a) and (c), while the proposed CS strategy is significantly better than rest of the algorithms (i.e., GA, IGA, and TS) for all three criteria. The obtained results indicate that the proposed CS technique can be an efficient and effective option for solving other similar NP-complete combinatorial problems also.

An Improved Genetic Algorithm and A New Discrete Cuckoo Algorithm for Solving the Classical Substitution Cipher

2019 ◽  
Vol 10 (2) ◽  
pp. 109-130 ◽  
Author(s):  
Ashish Jain ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Cuckoo Search ◽  
Combinatorial Problems ◽  
Secret Key ◽  
Improved Genetic Algorithm ◽  
Efficiency And Effectiveness ◽  
Np Complete ◽  
Metaheuristic Techniques ◽  
Better Than ◽  
Substitution Cipher

Searching secret key of classical ciphers in the keyspace is a challenging NP-complete problem that can be successfully solved using metaheuristic techniques. This article proposes two metaheuristic techniques: improved genetic algorithm (IGA) and a new discrete cuckoo search (CS) algorithm for solving a classical substitution cipher. The efficiency and effectiveness of the proposed techniques are compared to the existing tabu search (TS) and genetic algorithm (GA) techniques using three criteria: (a) average number of key elements correctly detected, (b) average number of keys examined before determining the required key, and (c) the mean performance time. As per the results obtained, the improved GA is comparatively better than the existing GA for criteria (a) and (c), while the proposed CS strategy is significantly better than rest of the algorithms (i.e., GA, IGA, and TS) for all three criteria. The obtained results indicate that the proposed CS technique can be an efficient and effective option for solving other similar NP-complete combinatorial problems also.

Based on Improved Genetic Algorithm for Task Scheduling of Heterogeneous Multi-Core Processor

2014 ◽  
Vol 1030-1032 ◽  
pp. 1671-1675
Author(s):  
Yue Qiu ◽  
Jing Feng Zang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Experimental Results ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
High Quality ◽  
Multi Core Processor ◽  
Better Than

This paper puts forward an improved genetic scheduling algorithm in order to improve the execution efficiency of task scheduling of the heterogeneous multi-core processor system and give full play to its performance. The attribute values and the high value of tasks were introduced to structure the initial population, randomly selected a method with the 50% probability to sort for task of individuals of the population, thus to get high quality initial population and ensured the diversity of the population. The experimental results have shown that the performance of the improved algorithm was better than that of the traditional genetic algorithm and the HEFT algorithm. The execution time of tasks was reduced.

The Study of Optimal Fuzzy PID-Smith Control Based on Genetic Algorithm

2013 ◽  
Vol 313-314 ◽  
pp. 448-452
Author(s):  
Dian Ting Liu ◽  
Hai Xia Li
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Control Method ◽  
Order System ◽  
Membership Functions ◽  
Fuzzy Pid ◽  
Improved Genetic Algorithm ◽  
Simulation Results ◽  
The Stability ◽  
Better Than

In this paper, the improved genetic algorithm is applied to optimize the quantization factors and the scaling factors of fuzzy control, and the optimized rule table and membership functions is obtained according to certain performances. Then a kind of optimal fuzzy PID-Smith control method based on genetic algorithm is proposed and its simulation model is built in this paper, a second-order system is simulated and analyzed. The results show that requirements of deterministic performances of the new control method are better than the conventional methods through the simulation results in the stability, rapidity and robustness.

scholarly journals Genetic Algorithm for Independent Job Scheduling in Grid Computing

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Muhanad Tahrir Younis ◽  
Shengxiang Yang
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Job Scheduling ◽  
Computational Power ◽  
New Mutation ◽  
Complete Problem ◽  
Geographically Distributed ◽  
Np Complete ◽  
And Storage ◽  
Better Than

Grid computing refers to the infrastructure which connects geographically distributed computers ownedby various organizations allowing their resources, such as computational power and storage capabilities, to beshared, selected, and aggregated. Job scheduling is the problem of mapping a set of jobs to a set of resources.It is considered one of the main steps to e ciently utilise the maximum capabilities of grid computing systems.The problem under question has been highlighted as an NP-complete problem and hence meta-heuristic methodsrepresent good candidates to address it. In this paper, a genetic algorithm with a new mutation procedure tosolve the problem of independent job scheduling in grid computing is presented. A known static benchmark forthe problem is used to evaluate the proposed method in terms of minimizing the makespan by carrying out anumber of experiments. The obtained results show that the proposed algorithm performs better than some knownalgorithms taken from the literature.

scholarly journals AN IMPROVED GENETIC ALGORITHM BASED FUZZY-TUNED NEURAL NETWORK

2005 ◽  
Vol 15 (06) ◽  
pp. 457-474 ◽  
Author(s):  
S. H. LING ◽  
F. H. F. LEUNG ◽  
H. K. LAM
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neuron Model ◽  
Activation Functions ◽  
Improved Genetic Algorithm ◽  
Network Function ◽  
Fuzzy Network ◽  
Neural Fuzzy Network ◽  
Neural Fuzzy ◽  
Better Than

This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA.

Implementation and Evaluation of the Image Segmentation Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1314-1317
Author(s):  
Li Jun Chen ◽  
Yong Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Entropy Method ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
And Performance ◽  
Standard Genetic Algorithm ◽  
Better Than

In order to achieve better image segmentation and evaluate the segmentation algorithm, a segmentation method based on 2-D maximum entropy and improved genetic algorithm is proposed in this paper, and the ultimate measurement accuracy criterion is adopted to evaluate the performance of the algorithm. The experimental results and the evaluation results show that segmentation results and performance of the proposed algorithm are both better than the segmentation method based on 2-D maximum entropy method and the standard genetic algorithm. The segmentation of the proposed algorithm is complete and spends less time; it is an effective method for image segmentation.

The Genetic Algorithm with Two Heuristic Rules for TSP

2013 ◽  
Vol 694-697 ◽  
pp. 2787-2793
Author(s):  
Yong Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solutions ◽  
Improved Genetic Algorithm ◽  
Heuristic Rules ◽  
Heuristic Rule ◽  
Hamiltonian Paths ◽  
Sequencing Problem ◽  
Discrete Manufacturing ◽  
Np Complete ◽  
Manufacturing Problems

Many complex discrete manufacturing problems, such as manufacturing sequencing problem or machine scheduling problem etc, can be converted into a general traveling salesman problem (TSP). TSP has been proven to be NP-complete. The genetic algorithm is improved with two heuristic rules for TSP. The first heuristic rule is the four vertices and three lines inequality. It is applied to the local Hamiltonian paths to generate the better solutions. The second heuristic rule is executed to reverse the local Hamiltonian paths, which generates new better solutions. The two heuristic rules coordinate with each other and they are merged into the optimization process of genetic algorithm to improve its performance. The computation results show that the improved genetic algorithm can find the near optimal solutions for most of the TSP instances.

scholarly journals Multiobjective Sustainable Order Allocation Problem Optimization with Improved Genetic Algorithm Using Priority Encoding

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Zhiqiang Fan ◽  
Shanshan Li ◽  
Zhijun Gao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Technique ◽  
Allocation Problem ◽  
Improved Genetic Algorithm ◽  
Order Allocation ◽  
Machine Material ◽  
Allocation Process ◽  
Efficiency And Effectiveness ◽  
Random Fluctuations

Recently, incorporating carbon emissions into order allocation decisions has attracted considerable attention among scholars and industrialists. Moreover, affected by the random fluctuations of the man, machine, material, method, and environment (4M1E), the production process is usually imperfect with defective products. Reducing product defective rates can effectively improve the quality of the order allocation process. Therefore, considering product defective rate and carbon emission, a multiobjective integer nonlinear programming (INLP) formulation is presented to address this multiproduct, multiperiod, and multi-OEM order allocation problem. Furthermore, exploring the existing literatures, an improved genetic algorithm using priority encoding (IGAUPE) is put forward as a novel optimization technique. Finally, numerical experiments are conducted to validate the correctness of the proposed INLP model as well as the effectiveness of the proposed algorithm. Compared with the genetic algorithm using binary encoding (GAUBE), genetic algorithm using two-layer encoding (GAUTE), and LINGO software, the experiment results show that IGAUPE can improve the efficiency and effectiveness within the predetermined time limit when solving large-scale instances.

scholarly journals Image Segmentation using Improved Genetic Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 1784-1792
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Approximate Solutions ◽  
Threshold Value ◽  
Complex Task ◽  
Metaheuristic Algorithm ◽  
Improved Genetic Algorithm ◽  
Complete Problem ◽  
Np Complete ◽  
Multi Level

Segmentation of image is a complex task. To recognize an image, segmentation is essential parts. During image segmentation, subsets of images on the basis of some features like gray levels values of pixels or position of pixels find out. This is an NP-complete problem, to find the solution to such problems is a computationally hard task. Some heuristic algorithm can be used to find out the solution to such a hard task. These algorithms find approximate solutions. Exact solution of such problems is not possible. Genetic algorithm can be considered a metaheuristic algorithm used the evolution of the population of solutions. This paper proposedan improved Genetic Algorithm that used to find multi-level thresholding segmentation of the image. The threshold value can be calculated by cumulative histogram and satisfactory result have been given by the experiments done on test images that are taken from Mnist datasets

The Research of the Genetic Algorithm Combined with Chromosome Fitness to Optimize the Flatness Error Evaluation

2013 ◽  
Vol 278-280 ◽  
pp. 1342-1348
Author(s):  
Ying Di Guo ◽  
Wei Cong Wu ◽  
Mi Jia Jiang ◽  
Bo Nan Li ◽  
Bing Bing Fang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Industrial Production ◽  
Measurement Accuracy ◽  
Error Evaluation ◽  
Improved Genetic Algorithm ◽  
Simulation Experiments ◽  
Flatness Error ◽  
Flatness Measurement ◽  
Better Than ◽  
The Ideal

This paper suggests an improved genetic algorithm to seek the minimum range value in the ideal-plane flatness measurement. This algorithm increases measurement accuracy by using dynamic cross factor, mutation factor and a new concept called chromosome fitness. It was proved in simulation experiments that its accuracy is better than other flatness error evaluating algorithms like the minimal territory evaluating algorithm and the computational geometry algorithm etc. So it can be used for measuring industrial production components error and verifying assumed models in reverse engineering etc.

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