scholarly journals Analysis of Selection Methods Used in Genetic Algorithms

2021 ◽  
Vol 4 ◽  
pp. 29-43
Author(s):  
Nataliya Gulayeva ◽  
Artem Ustilov
Keyword(s):  
Growth Rate ◽  
Genetic Algorithms ◽  
Selection Pressure ◽  
Fitness Function ◽  
Selection Method ◽  
Initial Population ◽  
Reproduction Rate ◽  
Selection Methods ◽  
Selection Scheme ◽  
Fitness Value

This paper offers a comprehensive review of selection methods used in the generational genetic algorithms.Firstly, a brief description of the following selection methods is presented: fitness proportionate selection methods including roulette-wheel selection (RWS) and its modifications, stochastic remainder selection with replacement (SRSWR), remainder stochastic independent selection (RSIS), and stochastic universal selection (SUS); ranking selection methods including linear and nonlinear rankings; tournament selection methods including deterministic and stochastic tournaments as well as tournaments with and without replacement; elitist and truncation selection methods; fitness uniform selection scheme (FUSS).Second, basic theoretical statements on selection method properties are given. Particularly, the selection noise, selection pressure, growth rate, reproduction rate, and computational complexity are considered. To illustrate selection method properties, numerous runs of genetic algorithms using the only selection method and no other genetic operator are conducted, and numerical characteristics of analyzed properties are computed. Specifically, to estimate the selection pressure, the takeover time and selection intensity are computed; to estimate the growth rate, the ratio of best individual copies in two consecutive populations is computed; to estimate the selection noise, the algorithm convergence speed is analyzed based on experiments carried out on a specific fitness function assigning the same fitness value to all individuals.Third, the effect of selection methods on the population fitness distribution is investigated. To do this, there are conducted genetic algorithm runs starting with a binomially distributed initial population. It is shown that most selection methods keep the distribution close to the original one providing an increased mean value of the distribution, while others (such as disruptive RWS, exponential ranking, truncation, and FUSS) change the distribution significantly. The obtained results are illustrated with the help of tables and histograms.

scholarly journals Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments

2008 ◽  
Vol 16 (3) ◽  
pp. 385-416 ◽  
Author(s):  
Shengxiang Yang
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Dynamic Environments ◽  
Population Diversity ◽  
Dynamic Problems ◽  
Dynamic Optimization Problems ◽  
Hybrid Memory ◽  
Memory Schemes ◽  
Memory Scheme ◽  
With Memory

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

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>

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.

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