scholarly journals A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm

2020 ◽  
Vol 10 (23) ◽  
pp. 8441
Author(s):  
Wende Li ◽  
Tinghua Ai ◽  
Yilang Shen ◽  
Wei Yang ◽  
Weilin Wang
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Process ◽  
Scale Reduction ◽  
Cartographic Generalization ◽  
Search Optimization ◽  
Manual Selection ◽  
Novel Method ◽  
Different Populations ◽  
Map Scale ◽  
Displacement Based

Owing to map scale reduction and other cartographic generalization operations, spatial conflicts may occur between buildings and other features in automatic cartographic generalization. Displacement is an effective map generalization operation to resolve these spatial conflicts to guarantee map clarity and legibility. In this paper, a novel building displacement method based on multipopulation genetic algorithm (BDMPGA) is proposed to resolve spatial conflicts. This approach introduces multiple populations with different control parameters for simultaneous search optimization and adopts an immigration operation to connect different populations to realize coevolution. The optimal individuals of each population are selected and preserved in the elite population through manual selection operation to prevent the optimal individuals from being destroyed and lost in the evolutionary process. Meanwhile, the least preserving generation of the optimal individuals is used as the termination basis. To validate the proposed method, urban building data with a scale of 1:10,000 from Shenzhen, China are used. The experimental results indicate that the method proposed in this paper can effectively resolve spatial conflicts to obtain better results.

Automated Type Synthesis of Planar Mechanisms Using Numeric Optimization With Genetic Algorithms

2004 ◽  
Vol 127 (5) ◽  
pp. 910-916 ◽  
Author(s):  
Yi Liu ◽  
John McPhee
Keyword(s):  
Genetic Algorithm ◽  
Multibody Systems ◽  
General Type ◽  
Evolutionary Process ◽  
Type Synthesis ◽  
Genetic Operators ◽  
Planar Mechanisms ◽  
Optimization Framework ◽  
Automated Algorithm ◽  
Novel Method

This paper presents a novel method for the automated type synthesis of planar mechanisms and multibody systems. The method explicitly includes topology as a design variable in an optimization framework based on a genetic algorithm (GA). Each binary string genome of the GA represents the concatenation of the upper-right triangular portion of the link adjacency matrix of a mechanism. Different topologies can be explored by the GA by applying genetic operators to the genomes. The evolutionary process is not dependent on the results obtained from enumeration. Two examples of topology-based optimization show the applicability of this method to mechanism type synthesis problems. This method is distinct from others in the literature in that it represents the first fully automated algorithm for solving a general type synthesis problem with the help of a numeric optimizer.

scholarly journals Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

Research of Image Registration Based on Maximum Entropy Template Selection Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 1138-1143
Author(s):  
Hong Ying Qin
Keyword(s):  
Genetic Algorithm ◽  
Image Registration ◽  
Maximum Entropy ◽  
Evolutionary Process ◽  
Adaptive Genetic Algorithm ◽  
Image Stitching ◽  
Local Optimum ◽  
Selection Algorithm ◽  
Template Selection ◽  
Crossover And Mutation

This paper concerns an improved adaptive genetic algorithm, and the method is applied to the Maximum Entropy Template Selection Algorithm image registration. This method includes adjusting the probability of crossover and mutation in the evolutionary process. The method can overcome the disadvantage of traditional genetic algorithm that is easy to get into a local optimum answer. Results show our method is insensitive to the ordering, rotation and scale of the input images so it can be used in image stitching and retrieval of images & videos.

Genetic multistart algorithm for the design of fault-tolerant systems

2008 ◽  
Vol 222 (1) ◽  
pp. 17-29
Author(s):  
K Echtle ◽  
I Eusgeld ◽  
D Hirsch
Keyword(s):  
Genetic Algorithm ◽  
Fault Tolerance ◽  
Fault Tolerant ◽  
Design Method ◽  
Fitness Function ◽  
Evolutionary Process ◽  
Mechatronic Systems ◽  
Fast Evaluation ◽  
Pareto Solution ◽  
Fault Tolerant Systems

This paper presents a new approach to the multiobjective design of fault-tolerant systems. The design objectives are fault tolerance and cost. Reducing the cost is of particular importance for fault-tolerant systems because the overhead caused by redundant components is considerable. The new design method consists of a special genetic algorithm that is tailored to the particular issues of fault-tolerant systems. The interface of the present tool ePADuGA (elitist and Pareto-based Approach to Design fault-tolerant systems using a Genetic Algorithm) allows for adaptation to various fields of application. The degree of fault tolerance is measured by the number of tolerated faults rather than traditional reliability metrics, because reliability numbers are mostly unknown during early design phases. The special features of the genetic algorithm comprise a graph-oriented representation of systems (which are the individuals during the evolutionary process), a simple yet expressive fault model, a very efficient procedure for fault-tolerance evaluation, and a Pareto-oriented fitness function. In a genetic algorithm generating thousands of individuals, a very fast evaluation of each individual is mandatory. For this purpose, state-space-oriented evaluation methods have been cut down to an extremely simple function which is still sufficient to assess the fault tolerance of individuals. An innovative aspect is also a multistart technique to find a Pareto solution set, which is independent of any parameters. In this paper, experimental results are presented showing the feasibility of the approach as well as the usefulness of the final fault-tolerant architectures, particularly in the field of mechatronic systems.

scholarly journals Introducing modified version of penguins search optimization algorithm (PeSOA) and its application in optimal operation of reservoir systems

2017 ◽  
Vol 18 (4) ◽  
pp. 1484-1496 ◽  
Author(s):  
Afshin Mansouri ◽  
Babak Aminnejad ◽  
Hassan Ahmadi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Real World ◽  
Global Optimum ◽  
Optimal Operation ◽  
The Other ◽  
Reservoir System ◽  
Search Optimization ◽  
Real World Problem ◽  
Reservoir Systems

Abstract In the current study, modified version of the penguins search optimization algorithm (PeSOA) was introduced, and its usage was assessed in the water resources field. In the modified version (MPeSOA), the Gaussian exploration was added to the algorithm. The MPeSOA performance was evaluated in optimal operation of a hypothetical four-reservoir system and Karun-4 reservoir as a real world problem. Also, genetic algorithm (GA) was used as a criterion for evaluating the performance of PeSOA and MPeSOA. The results revealed that in a four-reservoir system problem, the PeSOA performance was much weaker than the GA; but on the other hand, the MPeSOA had better performance than the GA. In the mentioned problem, PeSOA, GA, and MPeSOA reached 78.43, 97.46, and 98.30% of the global optimum, respectively. In the operation of Karun-4 reservoir, although PeSOA performance had less difference with the two other algorithms than four-reservoir problem, its performance was not acceptable. The average values of objective function in this case were equal to 26.49, 23.84, and 21.48 for PeSOA, GA, and MPeSOA, respectively. According to the results obtained in the operation of Karun-4 reservoir, the algorithms including MPeSOA, GA, and PeSOA were situated in ranks one to three in terms of efficiency, respectively.

scholarly journals Information Theoretic Objective Function for Genetic Software Clustering

Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 18
Author(s):  
Habib Izadkhah ◽  
Mahjoubeh Tajgardan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Objective Function ◽  
High Performance ◽  
Evolutionary Process ◽  
Program Comprehension ◽  
Software Systems ◽  
Objective Functions ◽  
Information Theoretic ◽  
Software Clustering

Software clustering is usually used for program comprehension. Since it is considered to be the most crucial NP-complete problem, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness functions) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of the genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) to apply to another software graph such as artifact feature dependency graph. To overcome the existing objective functions’ limitations, this paper presents a new objective function. The new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.

scholarly journals Optimalisasi Penyelesaian Knapsack Problem Dengan Algoritma Genetika

2016 ◽  
pp. 182
Author(s):  
I Wayan Supriana
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Everyday Life ◽  
Knapsack Problem ◽  
Selection Process ◽  
Optimization Algorithms ◽  
Evolutionary Process ◽  
Knapsack Problems ◽  
Theory Of Evolution ◽  
Selection Of

Knapsack problems is a problem that often we encounter in everyday life. Knapsack problem itself is a problem where a person faced with the problems of optimization on the selection of objects that can be inserted into the container which has limited space or capacity. Problems knapsack problem can be solved by various optimization algorithms, one of which uses a genetic algorithm. Genetic algorithms in solving problems mimicking the theory of evolution of living creatures. The components of the genetic algorithm is composed of a population consisting of a collection of individuals who are candidates for the solution of problems knapsack. The process of evolution goes dimulasi of the selection process, crossovers and mutations in each individual in order to obtain a new population. The evolutionary process will be repeated until it meets the criteria o f an optimum of the resulting solution. The problems highlighted in this research is how to resolve the problem by applying a genetic algorithm knapsack. The results obtained by the testing of the system is built, that the knapsack problem can optimize the placement of goods in containers or capacity available. Optimizing the knapsack problem can be maximized with the appropriate input parameters.

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