scholarly journals Digital design and optimization of higher order adaptive system using Continuous genetic algorithm

2012 ◽  
Vol 1 (4) ◽  
pp. 290
Author(s):  
Vijay Yadav ◽  
Sarita Singh Bhadauria
Keyword(s):  
Genetic Algorithm ◽  
Rate Of Convergence ◽  
Adaptive System ◽  
Search Space ◽  
Higher Order ◽  
Digital Design ◽  
Solution Quality ◽  
Design And Optimization ◽  
Discrete Search ◽  
Continuous Genetic Algorithm

An algorithm for design and optimization of higher order adaptive system are presented in this paper. In this work, the algorithm applied on the continuous search space parameter rather than discrete search space parameter. A new continuous genetic operator such that Rank based selection, Normal crossover and mutations are used to improve the rate of convergence and solution quality has been proposed.

Optimized Crossover JumpX in Genetic Algorithm for General Routing Problems

2018 ◽  
pp. 205-230
Author(s):  
Hicham El Hassani ◽  
Said Benkachcha ◽  
Jamal Benhra
Keyword(s):  
Genetic Algorithm ◽  
Optimization Methods ◽  
Search Space ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Path Representation ◽  
Representation Technique ◽  
Hard Problems ◽  
The Traveling Salesman Problem

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied Supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. We adopt the path representation technique for our chromosome which is the most direct representation and a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.

scholarly journals Quality-Oriented Study on Mapping Island Model Genetic Algorithm onto CUDA GPU

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 318 ◽  
Author(s):  
Xue Sun ◽  
Ping Chou ◽  
Chao-Chin Wu ◽  
Liang-Rui Chen
Keyword(s):  
Genetic Algorithm ◽  
Execution Time ◽  
Large Scale ◽  
Parallel Architecture ◽  
Computation Time ◽  
Search Space ◽  
Island Model ◽  
Process Unit ◽  
Np Hard ◽  
Solution Quality

Genetic algorithm (GA), a global search method, has widespread applications in various fields. One very promising variant model of GA is the island model GA (IMGA) that introduces the key idea of migration to explore a wider search space. Migration will exchange chromosomes between islands, resulting in better-quality solutions. However, IMGA takes a long time to solve the large-scale NP-hard problems. In order to shorten the computation time, modern graphic process unit (GPU), as highly-parallel architecture, has been widely adopted in order to accelerate the execution of NP-hard algorithms. However, most previous studies on GPUs are focused on performance only, because the found solution qualities of the CPU and the GPU implementation of the same method are exactly the same. Therefore, it is usually previous work that did not report on quality. In this paper, we investigate how to find a better solution within a reasonable time when parallelizing IMGA on GPU, and we take the UA-FLP as a study example. Firstly, we propose an efficient approach of parallel tournament selection operator on GPU to achieve a better solution quality in a shorter amount of time. Secondly, we focus on how to tune three important parameters of IMGA to obtain a better solution efficiently, including the number of islands, the number of generations, and the number of chromosomes. In particular, different parameters have a different impact on solution quality improvement and execution time increment. We address the challenge of how to trade off between solution quality and execution time for these parameters. Finally, experiments and statistics are conducted to help researchers set parameters more efficiently to obtain better solutions when GPUs are used to accelerate IMGA. It has been observed that the order of influence on solution quality is: The number of chromosomes, the number of generations, and the number of islands, which can guide users to obtain better solutions efficiently with moderate increment of execution time. Furthermore, if we give higher priority on reducing execution time on GPU, the quality of the best solution can be improved by about 3%, with an acceleration that is 29 times faster than the CPU counterpart, after applying our suggested parameter settings. However, if we give solution quality a higher priority, i.e., the GPU execution time is close to the CPU’s, the solution quality can be improved up to 8%.

scholarly journals Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

2018 ◽  
Author(s):  
Morteza Madhkhan ◽  
Mohammad Reza Baradaran
Keyword(s):  
Genetic Algorithm ◽  
Rate Of Convergence ◽  
Optimal Solution ◽  
Search Space ◽  
Initial Population ◽  
Generation Process ◽  
Improved Genetic Algorithm ◽  
Cross Sectional ◽  
Design Constraints ◽  
Matlab Programming

Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language. The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions.

scholarly journals Changes in Personal Values in Pandemic Times

2021 ◽  
pp. 194855062110240
Author(s):  
Ella Daniel ◽  
Anat Bardi ◽  
Ronald Fischer ◽  
Maya Benish-Weisman ◽  
Julie A. Lee
Keyword(s):  
Longitudinal Data ◽  
Environmental Conditions ◽  
Adaptive System ◽  
Personal Values ◽  
Psychological Theory ◽  
Higher Order ◽  
Value Change ◽  
Openness To Change ◽  
Conservation Values ◽  
Self Transcendence

The COVID-19 pandemic has had immense impact on people’s lives, potentially leading individuals to reevaluate what they prioritize in life (i.e., their values). We report longitudinal data from Australians 3 years prior to the pandemic, at pandemic onset (April 2020, N = 2,321), and in November–December 2020 ( n = 1,442). While all higher order values were stable prior to the pandemic, conservation values, emphasizing order and stability, became more important during the pandemic. In contrast, openness to change values, emphasizing self-direction and stimulation, showed a decrease during the pandemic, which was reversed in late 2020. Self-transcendence values, emphasizing care for close others, society, and nature, decreased by late 2020. These changes were amplified among individuals worrying about the pandemic. The results support psychological theory of values as usually stable, but also an adaptive system that responds to significant changes in environmental conditions. They also test a new mechanism for value change, worry.

Minimum attribute reduction algorithm based on quick extraction and multi-strategy social spider optimization

2021 ◽  
pp. 1-16
Author(s):  
Qianjin Wei ◽  
Chengxian Wang ◽  
Yimin Wen
Keyword(s):  
Rough Set Theory ◽  
Attribute Reduction ◽  
Search Space ◽  
Initial State ◽  
Solution Quality ◽  
Social Spider ◽  
Opposition Based Learning ◽  
Social Spider Optimization ◽  
Intelligent Optimization Algorithm ◽  
Redundancy Detection

Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

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