scholarly journals Results of Evolution Supervised by Genetic Algorithms

2010 ◽  
Vol 2 (3) ◽  
pp. 12-15 ◽  
Author(s):  
Lorentz JÄNTSCHI ◽  
Sorana D. BOLBOACĂ ◽  
Mugur C. BĂLAN ◽  
Radu E. SESTRAS ◽  
Mircea V. DIUDEA
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Solution ◽  
Structure Characterization ◽  
Survival Strategies ◽  
Selection Strategy ◽  
Structure Activity Relationships ◽  
Mutation Operators ◽  
Structure Activity ◽  
Survival Selection

The efficiency of a genetic algorithm is frequently assessed using a series of operators of evolution like crossover operators, mutation operators or other dynamic parameters. The present paper aimed to review the main results of evolution supervised by genetic algorithms used to identify solutions to agricultural and horticultural hard problems and to discuss the results of using a genetic algorithms on structure-activity relationships in terms of behavior of evolution supervised by genetic algorithms. A genetic algorithm had been developed and implemented in order to identify the optimal solution in term of estimation power of a multiple linear regression approach for structure-activity relationships. Three survival and three selection strategies (proportional, deterministic and tournament) were investigated in order to identify the best survival-selection strategy able to lead to the model with higher estimation power. The Molecular Descriptors Family for structure characterization of a sample of 206 polychlorinated biphenyls with measured octanol-water partition coefficients was used as case study. Evolution using different selection and survival strategies proved to create populations of genotypes living in the evolution space with different diversity and variability. Under a series of criteria of comparisons these populations proved to be grouped and the groups were showed to be statistically different one to each other. The conclusions about genetic algorithm evolution according to a number of criteria were also highlighted.

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.

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.

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.

scholarly journals Many-Objective Optimization and Decision-Making Method for Selective Assembly of Complex Mechanical Products Based on Improved NSGA-III and VIKOR

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 34
Author(s):  
Rongshun Pan ◽  
Jiahao Yu ◽  
Yongman Zhao
Keyword(s):  
Success Rate ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Optimal Solution ◽  
Solution Set ◽  
Selection Strategy ◽  
Selective Assembly ◽  
Assembly Quality ◽  
Mutation Operators ◽  
Mechanical Products

In Industry 4.0, data are sensed and merged to drive intelligent systems. This research focuses on the optimization of selective assembly of complex mechanical products (CMPs) under intelligent system environment conditions. For the batch assembly of CMPs, it is difficult to obtain the best combinations of components from combinations for simultaneous optimization of success rate and multiple assembly quality. Hence, the Taguchi quality loss function was used to quantitatively evaluate each assembly quality and the assembly success rate is combined to establish a many-objective optimization model. The crossover and mutation operators were improved to enhance the ability of NSGA-III to obtain high-quality solution set and jump out of a local optimal solution, and the Pareto optimal solution set was obtained accordingly. Finally, considering the production mode of Human–Machine Intelligent System interaction, the optimal compromise solution is obtained by using fuzzy theory, entropy theory and the VIKOR method. The results show that this work has obvious advantages in improving the quality of batch selective assembly of CMPs and assembly success rate and gives a sorting selection strategy for non-dominated selective assembly schemes while taking into account the group benefit and individual regret.

scholarly journals An enhanced hybrid genetic algorithm for solving traveling salesman problem

2020 ◽  
Vol 18 (2) ◽  
pp. 1035
Author(s):  
Zeravan Arif Ali ◽  
Subhi Ahmed Rasheed ◽  
Nabeel No’man Ali
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Mutation Operators ◽  
Np Hard Problem ◽  
The Traveling Salesman Problem

<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

scholarly journals Parallel genetic algorithms on the graphics processing units using island model and simulated annealing

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401770741 ◽  
Author(s):  
Cheng-Chieh Li ◽  
Chu-Hsing Lin ◽  
Jung-Chun Liu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Graphics Processing Units ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Optimization Method ◽  
Approximate Algorithm ◽  
Parallel Genetic Algorithms ◽  
Graphics Processing

To solve a non-deterministic polynomial-hard problem, we can adopt an approximate algorithm for finding the near-optimal solution to reduce the execution time. Although this approach can come up with solutions much faster than brute-force methods, the downside of it is that only approximate solutions are found in most situations. The genetic algorithm is a global search heuristic and optimization method. Initially, genetic algorithms have many shortcomings, such as premature convergence and the tendency to converge toward local optimal solutions; hence, many parallel genetic algorithms are proposed to solve these problems. Currently, there exist many literatures on parallel genetic algorithms. Also, a variety of parallel genetic algorithms have been derived. This study mainly uses the advantages of graphics processing units, which has a large number of cores, and identifies optimized algorithms suitable for computation in single instruction, multiple data architecture of graphics processing units. Furthermore, the parallel simulated annealing method and spheroidizing annealing are also used to enhance performance of the parallel genetic algorithm.

scholarly journals A Variable-Length Chromosome Genetic Algorithm for Time-Based Sensor Network Schedule Optimization

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3990
Author(s):  
Van-Phuong Ha ◽  
Trung-Kien Dao ◽  
Ngoc-Yen Pham ◽  
Minh-Hoang Le
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Sensor Network ◽  
Optimal Solution ◽  
Stochastic Approach ◽  
Variable Length ◽  
Sensor Nodes ◽  
Fixed Length ◽  
Network Schedule ◽  
Application Requirements

Scheduling sensor nodes has an important role in real monitoring applications using sensor networks, lowering the power consumption and maximizing the network lifetime, while maintaining the satisfaction to application requirements. Nevertheless, this problem is usually very complex and not easily resolved by analytical methods. In a different manner, genetic algorithms (GAs) are heuristic search strategies that help to find the exact or approximate global optimal solution efficiently with a stochastic approach. Genetic algorithms are advantageous for their robustness to discrete and noisy objective functions, as they are only evaluated at independent points without requirements of continuity or differentiability. However, as explained in this paper, a time-based sensor network schedule cannot be represented by a chromosome with fixed length that is required in traditional genetic algorithms. Therefore, an extended genetic algorithm is introduced with variable-length chromosome (VLC) along with mutation and crossover operations in order to address this problem. Simulation results show that, with help of carefully defined fitness functions, the proposed scheme is able to evolve the individuals in the population effectively and consistently from generation to generation towards optimal ones, and the obtained network schedules are better optimized in comparison with the result of algorithms employing a fixed-length chromosome.

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