scholarly journals Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 390 ◽  
Author(s):  
Ahmad Hassanat ◽  
Khalid Almohammadi ◽  
Esra’a Alkafaween ◽  
Eman Abunawas ◽  
Awni Hammouri ◽  
...  
Keyword(s):  
Population Size ◽  
Optimization Problems ◽  
Large Population ◽  
Small Population ◽  
Mutation Rates ◽  
Mutation Operators ◽  
Tuning Methods ◽  
Two Parameters ◽  
The Impact ◽  
Crossover And Mutation

Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operators. This paper reviews various methods for choosing mutation and crossover ratios in GAs. Next, we define new deterministic control approaches for crossover and mutation rates, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic Decreasing of High Crossover (ILM/DHC). The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress, where (DHM/ILC) starts with 100% ratio for mutations, and 0% for crossovers. Both mutation and crossover ratios start to decrease and increase, respectively. By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers. (ILM/DHC) worked the same but the other way around. The proposed approach was compared with two parameters tuning methods (predefined), namely fifty-fifty crossover/mutation ratios, and the most common approach that uses static ratios such as (0.03) mutation rates and (0.9) crossover rates. The experiments were conducted on ten Traveling Salesman Problems (TSP). The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was found to be more effective when using large population size. In fact, both proposed dynamic methods outperformed the predefined methods compared in most cases tested.

scholarly journals The spatial Muller’s ratchet: surfing of deleterious mutations during range expansion

2019 ◽  
Author(s):  
Félix Foutel-Rodier ◽  
Alison Etheridge
Keyword(s):  
Population Size ◽  
Range Expansion ◽  
Allee Effect ◽  
Large Population ◽  
Small Population ◽  
Deleterious Mutations ◽  
Muller's Ratchet ◽  
Muller’S Ratchet ◽  
Expansion Load ◽  
The Impact

AbstractDuring a range expansion, deleterious mutations can “surf” on the colonisation front. The resultant decrease in fitness is known as expansion load. An Allee effect is known to reduce the loss of genetic diversity of expanding populations, by changing the nature of the expansion from “pulled” to “pushed”. We study the impact of an Allee effect on the formation of an expansion load with a new model, in which individuals have the genetic structure of a Muller’s ratchet. A key feature of Muller’s ratchet is that the population fatally accumulates deleterious mutations due to the stochastic loss of the fittest individuals, an event called a click of the ratchet. We observe fast clicks of the ratchet at the colonization front owing to small population size, followed by a slow fitness recovery due to migration of fit individuals from the bulk of the population, leading to a transient expansion load. For large population size, we are able to derive quantitative features of the expansion wave, such as the wave speed and the frequency of individuals carrying a given number of mutations. Using simulations, we show that the presence of an Allee effect reduces the rate at which clicks occur at the front, and thus reduces the expansion load.

scholarly journals Population size influences the type of nucleotide variations in humans

BMC Genetics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Sankar Subramanian
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
High Frequency ◽  
Genetic Diseases ◽  
Large Population ◽  
Low Frequency ◽  
Small Population ◽  
Effective Population ◽  
Genome Data ◽  
Large Populations

Abstract Background It is well known that the effective size of a population (Ne) is one of the major determinants of the amount of genetic variation within the population. However, it is unclear whether the types of genetic variations are also dictated by the effective population size. To examine this, we obtained whole genome data from over 100 populations of the world and investigated the patterns of mutational changes. Results Our results revealed that for low frequency variants, the ratio of AT→GC to GC→AT variants (β) was similar across populations, suggesting the similarity of the pattern of mutation in various populations. However, for high frequency variants, β showed a positive correlation with the effective population size of the populations. This suggests a much higher proportion of high frequency AT→GC variants in large populations (e.g. Africans) compared to those with small population sizes (e.g. Asians). These results imply that the substitution patterns vary significantly between populations. These findings could be explained by the effect of GC-biased gene conversion (gBGC), which favors the fixation of G/C over A/T variants in populations. In large population, gBGC causes high β. However, in small populations, genetic drift reduces the effect of gBGC resulting in reduced β. This was further confirmed by a positive relationship between Ne and β for homozygous variants. Conclusions Our results highlight the huge variation in the types of homozygous and high frequency polymorphisms between world populations. We observed the same pattern for deleterious variants, implying that the homozygous polymorphisms associated with recessive genetic diseases will be more enriched with G or C in populations with large Ne (e.g. Africans) than in populations with small Ne (e.g. Europeans).

scholarly journals A logistic-harvest model with allee effect under multiplicative noise

2021 ◽  
pp. 2150044
Author(s):  
Almaz Tesfay ◽  
Daniel Tesfay ◽  
James Brannan ◽  
Jinqiao Duan
Keyword(s):  
Population Size ◽  
Carrying Capacity ◽  
Noise Intensity ◽  
Allee Effect ◽  
Large Population ◽  
Logistic Growth ◽  
Maximum Point ◽  
Harvest Rate ◽  
Harvest Model ◽  
The Impact

This work is devoted to the study of a stochastic logistic growth model with and without the Allee effect. Such a model describes the evolution of a population under environmental stochastic fluctuations and is in the form of a stochastic differential equation driven by multiplicative Gaussian noise. With the help of the associated Fokker–Planck equation, we analyze the population extinction probability and the probability of reaching a large population size before reaching a small one. We further study the impact of the harvest rate, noise intensity and the Allee effect on population evolution. The analysis and numerical experiments show that if the noise intensity and harvest rate are small, the population grows exponentially, and upon reaching the carrying capacity, the population size fluctuates around it. In the stochastic logistic-harvest model without the Allee effect, when noise intensity becomes small (or goes to zero), the stationary probability density becomes more acute and its maximum point approaches one. However, for large noise intensity and harvest rate, the population size fluctuates wildly and does not grow exponentially to the carrying capacity. So as far as biological meanings are concerned, we must catch at small values of noise intensity and harvest rate. Finally, we discuss the biological implications of our results.

scholarly journals Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Guojiang Xiong ◽  
Jing Zhang ◽  
Dongyuan Shi ◽  
Xufeng Yuan
Keyword(s):  
Population Size ◽  
Optimization Problems ◽  
Supply And Demand ◽  
Parameter Extraction ◽  
Small Population ◽  
Model Parameters ◽  
Solar Photovoltaic ◽  
Pv Systems ◽  
Highly Nonlinear ◽  
Optimal Values

Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.

Data processing with an Improved Hybrid Optimization Algorithm Base on PSO-GA

2014 ◽  
Vol 1014 ◽  
pp. 404-412 ◽  
Author(s):  
Fu Kun Zhang ◽  
Shu Wen Zhang ◽  
Gui Zhi Ba
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Hybrid Optimization ◽  
Swarm Optimization ◽  
Algorithm Performance ◽  
Hybrid Optimization Algorithm ◽  
Exchange Information ◽  
Algorithm Base ◽  
The Impact ◽  
Crossover And Mutation

This paper develops an improved hybrid optimization algorithm based on particle swarm optimization (PSO) and a genetic algorithm (GA). First, the population is evolved over a certain number of generations by PSO and the best M particles are retained, with the remaining particles excluded. Second, new individuals are generated by implementing selection, crossover and mutation GA operators for the best M particles. Finally, the new individuals are combined with the best M particles to form new a population for the next generation. The algorithm can exchange information several times during evolution so that the complement of two algorithms can be more fully exploited. The proposed method is applied to fifteen benchmark optimization problems and the results obtained show an improvement over published methods. The impact of M on algorithm performance is also discussed.

scholarly journals Evolutionary Method of Detecting Intrusions using Different Population Dynamics

2020 ◽  
Vol 9 (5) ◽  
pp. 1924-1931
Keyword(s):  
Population Dynamics ◽  
Population Size ◽  
Large Population ◽  
Computation Time ◽  
Small Population ◽  
Vital Role ◽  
Local Optima ◽  
Population Sizes ◽  
Speed Up ◽  
Dynamic Population

Chaos theory plays a vital role in any evolutionary based algorithms for avoiding the local optima and to improve the convergence speed. Various researchers have used different methods to increase the detection rate and to speed up the convergence. Some researchers have used evolutionary algorithms for the same purpose and has proved that the application of those algorithms provide good results. Most of the researchers have used population sizes which remains constant throughout the evolution. It has been seen that small population size may result in premature convergence and large population size requires more computation time to find a solution. In this paper, a novel application of different population dynamics to the genetic programming (GP) algorithm has been applied to manage the population size. The main focus was to improve the accuracy of the normal GP algorithm by varying the population sizes at each generation. The experiments were conducted on the standard GP algorithm using static and dynamic population sizes. Different population dynamics has been used to check the effectiveness of the proposed algorithm. The results obtained has shown that dynamic population size gives better results compared to static population size and also solves the problem of local optima.

scholarly journals Clear: Composition of Likelihoods for Evolve And Resequence Experiments

2016 ◽  
Author(s):  
Arya Iranmehr ◽  
Ali Akbari ◽  
Christian Schlötterer ◽  
Vineet Bafna
Keyword(s):  
Population Size ◽  
Time Series Data ◽  
Large Population ◽  
Small Population ◽  
Whole Genome Sequence ◽  
Series Data ◽  
Model Parameters ◽  
Whole Genome ◽  
Population Sampling ◽  
Wide Range

AbstractThe advent of next generation sequencing technologies has made whole-genome and whole-population sampling possible, even for eukaryotes with large genomes. With this development, experimental evolution studies can be designed to observe molecular evolution “in-action” via Evolve-and-Resequence (E&R) experiments. Among other applications, E&R studies can be used to locate the genes and variants responsible for genetic adaptation. Existing literature on time-series data analysis often assumes large population size, accurate allele frequency estimates, and wide time spans. These assumptions do not hold in many E&R studies.In this article, we propose a method-Composition of Likelihoods for Evolve-And-Resequence experiments (Clear)–to identify signatures of selection in small population E&R experiments. Clear takes whole-genome sequence of pool of individuals (pool-seq) as input, and properly addresses heterogeneous ascertainment bias resulting from uneven coverage. Clear also provides unbiased estimates of model parameters, including population size, selection strength and dominance, while being computationally efficient. Extensive simulations show that Clear achieves higher power in detecting and localizing selection over a wide range of parameters, and is robust to variation of coverage. We applied Clear statistic to multiple E&R experiments, including, data from a study of D. melanogaster adaptation to alternating temperatures and a study of outcrossing yeast populations, and identified multiple regions under selection with genome-wide significance.

Optimal Configuration of a Genetic Algorithm for Command Space Layout Analysis

2018 ◽  
Vol 62 (1) ◽  
pp. 383-387 ◽  
Author(s):  
Wenbi Wang
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Simulation Experiment ◽  
Large Population ◽  
Spatial Layout ◽  
Search Efficiency ◽  
Medium Level ◽  
Military Command ◽  
Space Layout ◽  
The Impact

A genetic algorithm was developed to optimize the spatial layout of military command centres. This paper describes a simulation experiment in which the impact of key algorithm parameters on its search efficiency was examined. The results confirmed the benefit of a large population size and a long evolution process for improving the search effectiveness. For the parameter that controls the rate of introducing new solutions (i.e., probability of swap), a medium level configuration was found to be superior. Results of this study provide guidelines and heuristics for configuring key parameters of the proposed algorithm so that its search efficiency and computational expense are best balanced.

scholarly journals THE PROPOSAL FOR MONITORING OF GOLDEN JACKAL (Canis aureus)

2020 ◽  
Vol 357 (56) ◽  
pp. 31-44
Author(s):  
Oliwia MAŃKO ◽  
Keyword(s):  
Population Size ◽  
Native Species ◽  
Small Population ◽  
Golden Jackal ◽  
Monitoring Method ◽  
Canis Aureus ◽  
Monitoring Process ◽  
The Future ◽  
Access To Water ◽  
The Impact

Golden jackal (Canis aureus) is a mesopredator. As an opportunistic species, it can both compete and pose a threat to native species. The golden jackal was first documented in Poland in 2015, where it came probably due to the natural expansion of the species distribution range. Currently, its estimated population size is based only on observations of single individuals, but this may change in the future. The recent expansion of the golden jackal, as well as its small population size in Poland, result in a low level of knowledge about this species and its impact on the native fauna and flora. The purpose of monitoring is to help in the future control of the population size, as well as to facilitate the acquisition of knowledge on the biology and the impact of this species on the environment. The monitoring method of the golden jackal presented in this article consists of the assessment of both the species’ habitat and its population. Overall, the proposed assessment of the habitat and population is based on evaluation of 7 indicators (population density, number of litters, height above sea level, presence of wolves, access to water reservoirs, scrubs, food base availability). Indicator assessment allows to determine, whether a given site is favorable for the settlement and growth of the golden jackal population. Observations carried out during the monitoring process may additionally facilitate the recognition of the species in the newly occupied areas, and allow to determine its impact on the environment.

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