User’s Preference Aggregation Based on Parallel Interactive Genetic Algorithms

2010 ◽  
Vol 34-35 ◽  
pp. 1159-1164 ◽  
Author(s):  
Yi Nan Guo ◽  
Yong Lin ◽  
Mei Yang ◽  
Shu Guo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Solution ◽  
Small Population ◽  
Population Diversity ◽  
Design System ◽  
Preference Aggregation ◽  
Ip Multicast ◽  
Interactive Genetic Algorithm ◽  
Interactive Genetic Algorithms

In traditional interactive genetic algorithms, high-quality optimal solution is hard to be obtained due to small population size and limited evolutional generations. Aming at above problems, a parallel interactive genetic algorithm based on knowledge migration is proposed. During the evolution, the number of the populations is more than one. Evolution information can be exchanged between every two populations so as to guide themselves evolution. In order to realize the freedom communication, IP multicast is adopted as the transfer protocol to find out the similar users instead of traditional TCP/IP communication mode. Taken the fashion evolutionary design system as test platform, the results indicate that the IP multicast-based parallel interactive genetic algorithm has better population diversity. It also can alleviate user fatigue and speed up the convergence.

Multi-Operator Genetic Algorithm for Dynamic Optimization Problems

2017 ◽  
Vol 6 (3) ◽  
pp. 139
Author(s):  
Al-khafaji Amen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Population Diversity ◽  
Dynamic Optimization Problems ◽  
Uncertain Environments ◽  
Convergence Problem ◽  
Royal Road

<span lang="EN-US">Maintaining population diversity is the most notable challenge in solving dynamic optimization problems (DOPs). Therefore, the objective of an efficient dynamic optimization algorithm is to track the optimum in these uncertain environments, and to locate the best solution. In this work, we propose a framework that is based on multi operators embedded in genetic algorithms (GA) and these operators are heuristic and arithmetic crossovers operators. The rationale behind this is to address the convergence problem and to maintain the diversity. The performance of the proposed framework is tested on the well-known dynamic optimization functions i.e., OneMax, Plateau, Royal Road and Deceptive. Empirical results show the superiority of the proposed algorithm when compared to state-of-the-art algorithms from the literature.</span>

scholarly journals A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Adaptive Mutation ◽  
Local Optima ◽  
3 Dimensional ◽  
Successive Generations

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

A New Testability Optimization Allocation Approach

2013 ◽  
Vol 328 ◽  
pp. 444-449 ◽  
Author(s):  
Gang Liu ◽  
Fang Li
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Iterative Process ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Numerical Example ◽  
Set Up ◽  
Optimization Allocation ◽  
Allocation Approach

This paper describes a methodology based on improved genetic algorithms (GA) and experiments plan to optimize the testability allocation. Test resources were reasonably configured for testability optimization allocation, in order to meet the testability allocation requirements and resource constraints. The optimal solution was not easy to solve of general genetic algorithm, and the initial parameter value was not easy to set up and other defects. So in order to more efficiently test and optimize the allocation, migration technology was introduced in the traditional genetic algorithm to optimize the iterative process, and initial parameters of algorithm could be adjusted by using AHP approach, consequently testability optimization allocation approach based on improved genetic algorithm was proposed. A numerical example is used to assess the method. and the examples show that this approach can quickly and efficiently to seek the optimal solution of testability optimization allocation problem.

scholarly journals Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Novel Mutation ◽  
Fitness Function ◽  
Genetic Disorders ◽  
Optimal Solution ◽  
Global Optimum ◽  
Adaptive Mutation ◽  
Local Optima

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals A COMPARISON OF SIMULATED ANNEALING, GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION IN OPTIMAL FIRST-ORDER DESIGN OF INDOOR TLS NETWORKS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 75-82 ◽  
Author(s):  
F. Jia ◽  
D. Lichti
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Network Design Problem ◽  
Design System ◽  
Swarm Optimization ◽  
First Order

The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn’t guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.

Research on Multimodal Transport Shortest Path Based on the Genetic Algorithm

2015 ◽  
Vol 744-746 ◽  
pp. 1813-1816
Author(s):  
Shou Wen Ji ◽  
Shi Jin ◽  
Kai Lv
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Shortest Path ◽  
Optimal Solution ◽  
Path Model ◽  
Time Analysis ◽  
Multimodal Transportation ◽  
Network Time ◽  
Multimodal Transport ◽  
Crossover And Mutation

This paper focuses on the research of multimodal transportation optimization model and algorithm, designs an intermodal shortest time path model and gives a solution to algorithm, constructs a multimodal transport network time analysis chart. By using genetic algorithms, the transportation scheme will be optimized. And based on each path’s code, the population will be evolved to obtain the optimal solution by using crossover and mutation rules.

Optimization Design of the Crane Girder Based on Adaptive Genetic Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 123-126
Author(s):  
Peng Fei Wang ◽  
Xiu Hui Diao
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Variable ◽  
Optimization Design ◽  
Optimal Solution ◽  
Population Diversity ◽  
Main Beam ◽  
Gantry Crane ◽  
Adaptive Genetic Algorithm ◽  
Accelerate Convergence

With taking weight of single main beam of gantry crane as objective function, and taking main beam upper & lower cored, diagonal & horizontal bracing, and width & weight as design variable, this essay adopted population diversity adaptive genetic algorithm to optimize its structure and improved program design through MATLAB. This algorithm could accelerate convergence speed, which make much it easier to realize comprehensive optimal solution, since it effectively avoided weakness of basic genetic algorithm, such as partial optimal solution, prematurity and being lack of continuity, etc.

Researches on Function Optimization Problems Based on Chaotic Immune Genetic Algorithms

2012 ◽  
Vol 616-618 ◽  
pp. 2064-2067
Author(s):  
Yong Gang Che ◽  
Chun Yu Xiao ◽  
Chao Hai Kang ◽  
Ying Ying Li ◽  
Li Ying Gong
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Chaotic Map ◽  
Solution Space ◽  
Population Diversity ◽  
Function Optimization ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Complex Functions

To solve the primary problems in genetic algorithms, such as slow convergence speed, poor local searching capability and easy prematurity, the immune mechanism is introduced into the genetic algorithm, and thus population diversity is maintained better, and the phenomena of premature convergence and oscillation are reduced. In order to compensate the defects of immune genetic algorithm, the Hénon chaotic map, which is introduced on the above basis, makes the generated initial population uniformly distributed in the solution space, eventually, the defect of data redundancy is reduced and the quality of evolution is improved. The proposed chaotic immune genetic algorithm is used to optimize the complex functions, and there is an analysis compared with the genetic algorithm and the immune genetic algorithm, the feasibility and effectiveness of the proposed algorithm are proved from the perspective of simulation experiments.

Study on the Rapid Design and Optimization Technology of Forklift Styling

2011 ◽  
Vol 66-68 ◽  
pp. 944-949
Author(s):  
Huan Su ◽  
Shou Qian Sun ◽  
Hai Hua Ren ◽  
Xiao Jian Liu
Keyword(s):  
Genetic Algorithms ◽  
Parameter Optimization ◽  
Design System ◽  
Design And Optimization ◽  
Programming Tools ◽  
Interactive Genetic Algorithms ◽  
Model Surfaces ◽  
New Models ◽  
Computer Aided ◽  
Different Color

The paper introduced a computer-aided industrial design system: Forklift Truck’s Multi Plan Optimizaion System, and demonstrated the modules’ function and techniques with examples. Considering the requirements for efficiency and precision in the forklift styling, the author developed the forklift optimizing software for its styling, which includes parameter optimization, color optimization and part combination three modules. The parameter optimization module can vary the user defined parameters of the forklift models and generate new models. Color optimization module can group the model surfaces and render the groups with different color. New color plans are generated through random changing of the colors in each group. The parts combination module divides forklift into several parts and build lib for each part. The module can pick parts from the lib and assemble them into a whole forklift and demonstrate them. The thesis developed a proto system on the Solidworks platform with VBA programming tools. Interactive genetic algorithms are applied to realize the three module’s function.

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