scholarly journals OPTIMIZATION OF 2D LATTICE CELLULAR AUTOMATA FOR PSEUDORANDOM NUMBER GENERATION

2005 ◽  
Vol 16 (03) ◽  
pp. 479-500 ◽  
Author(s):  
MARIE THERESE ROBLES QUIETA ◽  
SHENG-UEI GUAN
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Neighborhood Characteristics ◽  
Structure And Properties ◽  
Pseudorandom Number Generation ◽  
The Cost ◽  
Number Of Cells ◽  
Better Than ◽  
2D Array

This paper proposes a generalized approach to 2D CA PRNGs — the 2D lattice CA PRNG — by introducing vertical connections to arrays of 1D CA. The structure of a 2D lattice CA PRNG lies in between that of 1D CA and 2D CA grid PRNGs. With the generalized approach, 2D lattice CA PRNG offers more 2D CA PRNG variations. It is found that they can do better than the conventional 2D CA grid PRNGs. In this paper, the structure and properties of 2D lattice CA are explored by varying the number and location of vertical connections, and by searching for different 2D array settings that can give good randomness based on Diehard test. To get the most out of 2D lattice CA PRNGs, genetic algorithm is employed in searching for good neighborhood characteristics. By adopting an evolutionary approach, the randomness quality of 2D lattice CA PRNGs is optimized. In this paper, a new metric, #rn is introduced as a way of finding a 2D lattice CA PRNG with the least number of cells required to pass Diehard test. Following the introduction of the new metric #rn, a cropping technique is presented to further boost the CA PRNG performance. The cost and efficiency of 2D lattice CA PRNG is compared with past works on CA PRNGs.

scholarly journals A FAMILY OF CONTROLLABLE CELLULAR AUTOMATA FOR PSEUDORANDOM NUMBER GENERATION

2002 ◽  
Vol 13 (08) ◽  
pp. 1047-1073 ◽  
Author(s):  
SHENG-UEI GUAN ◽  
SHU ZHANG
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Pseudorandom Number ◽  
Test Results ◽  
Randomness Test ◽  
Different Types ◽  
Pseudorandom Number Generation ◽  
Test Suites ◽  
Better Than

In this paper, we present a family of novel Pseudorandom Number Generators (PRNGs) based on Controllable Cellular Automata (CCA) CCA0, CCA1, CCA2 (NCA), CCA3 (BCA), CCA4 (asymmetric NCA), CCA5, CCA6 and CCA7 PRNGs. The ENT and DIEHARD test suites are used to evaluate the randomness of these CCA PRNGs. The results show that their randomness is better than that of conventional CA and PCA PRNGs while they do not lose the structure simplicity of 1D CA. Moreover, their randomness can be comparable to that of 2D CA PRNGs. Furthermore, we integrate six different types of CCA PRNGs to form CCA PRNG groups to see if the randomness quality of such groups could exceed that of any individual CCA PRNG. Genetic Algorithm (GA) is used to evolve the configuration of the CCA PRNG groups. Randomness test results on the evolved CCA PRNG groups show that the randomness of the evolved groups is further improved as compared with any individual CCA PRNG.

scholarly journals A Heuristics-Based Parthenogenetic Algorithm for the VRP with Potential Demands and Time Windows

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Chenghua Shi ◽  
Tonglei Li ◽  
Yu Bai ◽  
Fei Zhao
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Computation Time ◽  
Routing Problem ◽  
Split Delivery ◽  
Soft Time Windows ◽  
Comparison Results ◽  
Potential Demand ◽  
The Cost

We present the vehicle routing problem with potential demands and time windows (VRP-PDTW), which is a variation of the classical VRP. A homogenous fleet of vehicles originated in a central depot serves customers with soft time windows and deliveries from/to their locations, and split delivery is considered. Also, besides the initial demand in the order contract, the potential demand caused by conformity consuming behavior is also integrated and modeled in our problem. The objective of minimizing the cost traveled by the vehicles and penalized cost due to violating time windows is then constructed. We propose a heuristics-based parthenogenetic algorithm (HPGA) for successfully solving optimal solutions to the problem, in which heuristics is introduced to generate the initial solution. Computational experiments are reported for instances and the proposed algorithm is compared with genetic algorithm (GA) and heuristics-based genetic algorithm (HGA) from the literature. The comparison results show that our algorithm is quite competitive by considering the quality of solutions and computation time.

scholarly journals Research on Optimal Matching Scheme of Public Resource Management Based on the Computational Intelligence Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Linna Li ◽  
Renjun Liu
Keyword(s):  
Genetic Algorithm ◽  
Human Resources ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Time Consumption ◽  
Public Resource ◽  
Public Resources ◽  
Benefit Function ◽  
The Cost ◽  
Better Than

The management of public resources means that people’s governments at all levels and other public administrative subjects should use certain means and methods, follow certain principles, rationally allocate and utilize public resources, and maximize their functions and benefits. Under the background of limited human resources, this study adheres to the principle of maximizing the benefits of human resources and rationally allocates the use of human resources. In this study, this kind of resource allocation problem is regarded as a linear programming problem by specifying the benefit function, and then, genetic algorithm, ant colony algorithm, and hybrid genetic-ant colony algorithm are used to solve the problem; the cost and time consumption of different algorithms under different scales are evaluated. Finally, it is found that genetic algorithm is superior to ant colony algorithm when the task scale is small and the effect of genetic algorithm is lower than ant colony algorithm with the expansion of task scale, whereas the improved hybrid genetic-ant colony algorithm is better than ordinary algorithm in general.

Adaptability and Homeostasis in the Game of Life interacting with the evolved Cellular Automata

2012 ◽  
pp. 232-254
Author(s):  
Keisuke Suzuki ◽  
Takashi Ikegami
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Space Time ◽  
Layer System ◽  
Game Of Life ◽  
Time Dynamic ◽  
State 1 ◽  
Number Of Cells

In this paper, the authors study the emergence of homeostasis in a two-layer system of the Game of Life, in which the Game of Life in the first layer couples with another system of cellular automata in the second layer. Homeostasis is defined as a space-time dynamic that regulates the number of cells in state-1 in the Game of Life layer. A genetic algorithm is used to evolve the rules of the second layer to control the pattern of the Game of Life. The authors found that two antagonistic attractors control the numbers of cells in state-1 in the first layer. The homeostasis sustained by these attractors is compared with the homeostatic dynamics observed in Daisy World.

Novel Compact Genetic Algorithm for WTA Problem

2012 ◽  
Vol 239-240 ◽  
pp. 1437-1441 ◽  
Author(s):  
Zhen Liu ◽  
Yun An Hu
Keyword(s):  
Genetic Algorithm ◽  
Experimental Results ◽  
The Novel ◽  
Simple Genetic Algorithm ◽  
Exchange Information ◽  
Compact Genetic Algorithm ◽  
Two Populations ◽  
Novel Algorithm ◽  
Better Than

The paper proposed a novel compact genetic algorithm which is named as pseudo-parallel compact genetic algorithm. There are two populations in the process of evolution, and the two subpopulation can exchange information between each other. The experimental results show that the novel algorithm performs better than simple genetic algorithm. Then it is used to solve weapon target allocation (WTA) problem, and the simulation result shows that it is more efficient comparing with other methods. Because the compact genetic algorithm is easy to operate and take up less memory, so the algorithm exhibit a better quality of solution and the required less time than before.

scholarly journals Genetic Algorithm for Biobjective Urban Transit Routing Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
J. S. C. Chew ◽  
L. S. Lee ◽  
H. V. Seow
Keyword(s):  
Genetic Algorithm ◽  
Feasible Solution ◽  
Benchmark Problems ◽  
Computational Results ◽  
Crossover Operator ◽  
Urban Transit ◽  
Routing Problem ◽  
Genetic Algorithm Approach ◽  
Better Than

This paper considers solving a biobjective urban transit routing problem with a genetic algorithm approach. The objectives are to minimize the passengers’ and operators’ costs where the quality of the route sets is evaluated by a set of parameters. The proposed algorithm employs an adding-node procedure which helps in converting an infeasible solution to a feasible solution. A simple yet effective route crossover operator is proposed by utilizing a set of feasibility criteria to reduce the possibility of producing an infeasible network. The computational results from Mandl’s benchmark problems are compared with other published results in the literature and the computational experiments show that the proposed algorithm performs better than the previous best published results in most cases.

scholarly journals A Parallel Mix Self-Adaptive Genetic Algorithm for Solving the Dynamic Economic Dispatch Problem

2020 ◽  
Author(s):  
Egidio De Carvalho Ribeiro Júnior ◽  
Omar Andres Carmona Cortes ◽  
Osvaldo Ronald Saavedra
Keyword(s):  
Genetic Algorithm ◽  
Economic Dispatch ◽  
Adaptive Genetic Algorithm ◽  
Random Algorithm ◽  
Dynamic Economic Dispatch ◽  
Crossover And Mutation ◽  
Adaptive Code ◽  
Better Than ◽  
Self Adaptive

The purpose of this paper is to propose a parallel genetic algorithm that has adaptive and self-adaptive characteristics at the same time for solving the Dynamic Economic Dispatch (DED) problem that is a challenging problem to solve. The algorithm selects the proper operators (using adaptive features) and probabilities (using the self-adaptive code) that produce the most fittable individuals. Regarding operations, the choice is made between four different types of crossover: simple, arithmetical, non-uniform arithmetical, and linear. Concerning mutation, we used four types of mutations (uniform, non-uniform, creep, and enhanced apso). The choice is made scholastically, which is uniform at the beginning of the algorithm, being adapted as the AG  executes. The crossover and mutation probabilities are coded into the genes, transforming this part of the algorithm into self-adaptive. The multicore version was coded using OpenMP. An ANOVA test, along with a Tukey test, proved that the mixed self-adaptive algorithm works better than both: a random algorithm, which chooses operators randomly, and a combination of operators set previously in the DED optimization. Regarding the performance of the parallel approach, results have shown that a speedup of up to 3.19 can be reached with no loss in the quality of solutions.

Micro- and nano-capsulated fungal pectinase with outstanding capabilities of eliminating turbidity in freshly produced juice

2018 ◽  
Vol 24 (4) ◽  
pp. 330-340 ◽  
Author(s):  
Khaled F Mahmoud ◽  
Heba I Abo-Elmagd ◽  
Manal M Housseiny
Keyword(s):  
Sodium Alginate ◽  
Orange Juice ◽  
Trichoderma Viride ◽  
Transmission Electron ◽  
Low Degrees ◽  
The Cost ◽  
Hydrolysis Of ◽  
Better Than ◽  
Alginate Matrix

The present study aimed to compare the pectinase forms produced from Trichoderma viride—free, micro-capsule, and nano-capsule—in sodium alginate to analyze the pectin that causes the turbidity of orange juice. This was performed along with an estimation of viscosity, residual of pectin, and turbidity. The extracted and purified enzyme was 24.35-fold better than that of the crude enzyme. After application of free one, it loses most of the activity on low degrees of acidity and remains constant on the temperatures of pasteurization. Therefore, the tested enzyme was encapsulated by two different ways using the same polymer. The morphology of the three pectinase forms was obtained by transmission electron microscopy, and the micrographs clearly showed the pores on the surface of sodium alginate matrix after encapsulation. The size of the wall (sodium alginate) ranged from 3.24 to 3.76 µm diameter but was 3.15 µm for core of enzyme. Micro-capsuled and nano-capsuled pectinase can be used in the hydrolysis of pectic substances in orange juice with natural ways and maintaining the quality of final product. Consequently, the cost of juice clarifying can be reduced due to reusing the enzyme several times.

Optimizing the Manufacturing of Laminated Dies

2009 ◽  
Author(s):  
Hossein Ahari ◽  
Amir Khajepour ◽  
Sanjeev Bedi
Keyword(s):  
Genetic Algorithm ◽  
Surface Quality ◽  
Laser Cutting ◽  
Production Costs ◽  
Layer By Layer ◽  
Promising Technology ◽  
Cooling Channels ◽  
Minimum Surface ◽  
The Cost

Laminated tooling where parts are manufactured layer by layer is a promising technology. It can help to reduce production costs and make complex tools with conformed cooling channels. Laminated tooling is based on taking sheets of metal and laser cutting profiles on them before stacking them to produce the final product. If the sheets are thin, the surface quality of final product will be good; however the cost of laser cut is increased in this case. Therefore, finding the optimum set of layers thicknesses to have the minimum surface jaggedness and the number of slices at the same is the aim of this research. A modified version of Genetic Algorithm is used for optimization purpose.

Adaptability and Homeostasis in the Game of Life interacting with the evolved Cellular Automata

2010 ◽  
Vol 1 (3) ◽  
pp. 40-50
Author(s):  
Keisuke Suzuki ◽  
Takashi Ikegami
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Space Time ◽  
Layer System ◽  
Game Of Life ◽  
Time Dynamic ◽  
State 1 ◽  
Number Of Cells

In this paper, the authors study the emergence of homeostasis in a two-layer system of the Game of Life, in which the Game of Life in the first layer couples with another system of cellular automata in the second layer. Homeostasis is defined as a space-time dynamic that regulates the number of cells in state-1 in the Game of Life layer. A genetic algorithm is used to evolve the rules of the second layer to control the pattern of the Game of Life. The authors found that two antagonistic attractors control the numbers of cells in state-1 in the first layer. The homeostasis sustained by these attractors is compared with the homeostatic dynamics observed in Daisy World.

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