Error Thresholds in Genetic Algorithms

2006 ◽  
Vol 14 (2) ◽  
pp. 157-182 ◽  
Author(s):  
Gabriela Ochoa
Keyword(s):  
Genetic Algorithms ◽  
Selection Pressure ◽  
Evolutionary Process ◽  
Critical Value ◽  
Mutation Rates ◽  
Genomic Information ◽  
Quasispecies Model ◽  
Error Catastrophe ◽  
Important Notion ◽  
Quasispecies Evolution

The error threshold of replication is an important notion in the quasispecies evolution model; it is a critical mutation rate (error rate) beyond which structures obtained by an evolutionary process are destroyed more frequently than selection can reproduce them. With mutation rates above this critical value, an error catastrophe occurs and the genomic information is irretrievably lost. Therefore, studying the factors that alter this magnitude has important implications in the study of evolution. Here we use a genetic algorithm, instead of the quasispecies model, as the underlying model of evolution, and explore whether the phenomenon of error thresholds is found on finite populations of bit strings evolving on complex landscapes. Our empirical results verify the occurrence of error thresholds in genetic algorithms. In this way, this notion is brought from molecular evolution to evolutionary computation. We also study the effect of modifying the most prominent evolutionary parameters on the magnitude of this critical value, and found that error thresholds depend mainly on the selection pressure and genotype length.

The Nesting of Two-Dimensional Shapes Using Genetic Algorithms

1995 ◽  
Vol 209 (2) ◽  
pp. 115-124 ◽  
Author(s):  
H S Ismail ◽  
K K B Hon
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Process ◽  
Search Space ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Spatial Constraints ◽  
Global Optimal ◽  
Optimum Solution ◽  
Rapid Conversion

The general two-dimensional cutting stock problem is concerned with the optimum layout and arrangement of two-dimensional shapes within the spatial constraints imposed by the cutting stock. The main objective is to maximize the utilization of the cutting stock material. This paper presents some of the results obtained from applying a combination of genetic algorithms and heuristic approaches to the nesting of dissimilar shapes. Genetic algorithms are stochastically based optimization approaches which mimic nature's evolutionary process in finding global optimal solutions in a large search space. The paper discusses the method by which the problem is defined and represented for analysis and introduces a number of new problem-specific genetic algorithm operators that aid in the rapid conversion to an optimum solution.

scholarly journals Intrinsic Noise and the Design of the Genetic Machinery

1983 ◽  
Vol 36 (1) ◽  
pp. 77 ◽  
Author(s):  
DC Reanney ◽  
DG MacPhee ◽  
J Pressing
Keyword(s):  
Life Style ◽  
Selection Pressure ◽  
Evolutionary Process ◽  
Intrinsic Noise ◽  
Darwinian Theory ◽  
Transmission Of Information ◽  
Genetic Mechanisms ◽  
Translational Mechanisms

Darwinian theory envisages 'selection pressure' as a stress imposed on the genotype by the environment. However, noise in the replicative and translational mechanisms in itself imposes a significant 'pressure' on the adaptive fitness of the organism. We propose that the biosphere has been shaped by both extrinsic (environmental) and intrinsic (noise-generated) factors. Because noise has been a remorseless and ever-present background to the evolutionary process, adaptations to this intrinsic pressure include not only a variety of familiar genetic mechanisms but also many anatomical and life-style characteristics that focus on the transmission of information between generations.

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.

The study of genetic information flux network properties in genetic algorithms

2015 ◽  
Vol 26 (07) ◽  
pp. 1550076
Author(s):  
Zhengping Wu ◽  
Qiong Xu ◽  
Gaosheng Ni ◽  
Gaoming Yu
Keyword(s):  
Genetic Algorithms ◽  
Empirical Analysis ◽  
Degree Distribution ◽  
Genetic Information ◽  
Selection Pressure ◽  
Selective Pressure ◽  
Node Degree ◽  
Scaling Exponent ◽  
Scale Free ◽  
Network Properties

In this paper, an empirical analysis is done on the information flux network (IFN) statistical properties of genetic algorithms (GA) and the results suggest that the node degree distribution of IFN is scale-free when there is at least some selection pressure, and it has two branches as node degree is small. Increasing crossover, decreasing the mutation rate or decreasing the selective pressure will increase the average node degree, thus leading to the decrease of scaling exponent. These studies will be helpful in understanding the combination and distribution of excellent gene segments of the population in GA evolving, and will be useful in devising an efficient GA.

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.

scholarly journals Concern for Dirofilaria immitis and Macrocyclic Lactone Loss of Efficacy: Current Situation in the USA and Europe, and Future Scenarios

Pathogens ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1323
Author(s):  
Anastasia Diakou ◽  
Roger K. Prichard
Keyword(s):  
Selection Pressure ◽  
Dirofilaria Immitis ◽  
Evolutionary Process ◽  
Resistance Development ◽  
Larval Stages ◽  
Resistance Selection ◽  
The Usa ◽  
Suppression Test ◽  
Resistant Strains ◽  
Geographical Point

Dirofilaria immitis infection is one of the most severe parasitic diseases in dogs. Prevention is achieved by the administration of drugs containing macrocyclic lactones (MLs). These products are very safe and highly effective, targeting the third and fourth larval stages (L3, L4) of the parasite. Until 2011, claims of the ineffectiveness of MLs, reported as “loss of efficacy” (LOE), were generally attributed to owners’ non-compliance, or other reasons associated with inadequate preventative coverage. There was solid argumentation that a resistance problem is not likely to occur because of (i) the great extent of refugia, (ii) the complexity of resistance development to MLs, and (iii) the possible large number of genes involved in resistance selection. Nevertheless, today, it is unequivocally proven that ML-resistant D. immitis strains exist, at least in the Lower Mississippi region, USA. Accordingly, tools have been developed to evaluate and confirm the susceptibility status of D. immitis strains. A simple, in-clinic, microfilariae suppression test, 14-28 days after ML administration, and a “decision tree” (algorithm), including compliance and preventatives’ purchase history, and testing gaps, may be applied for assessing any resistant nature of the parasite. On the molecular level, specific SNPs may be used as markers of ML resistance, offering a basis for the validation of clinically suspected resistant strains. In Europe, no LOE/resistance claims have been reported so far, and the existing conditions (stray dogs, rich wildlife, majority of owned dogs not on preventive ML treatment) do not favor selection pressure on the parasites. Considering the genetic basis of resistance and the epizootiological characteristics of D. immitis, ML resistance neither establishes easily nor spreads quickly, a fact confirmed by the current known dispersion of the problem, which is limited. Nevertheless, ML resistance may propagate from an initial geographical point, via animal and vector mobility, to other regions, while it can also emerge as an independent evolutionary process in a new area. For these reasons, and considering the current chemoprophylaxis recommendations and increasing use of ML endectoparasiticides as a potential selection pressure, it is important to remain vigilant for the timely detection of any ML LOE/resistance, in all continents where D. immitis is enzootic.

scholarly journals Concern for Dirofilaria immitis and Macrocyclic Lactone Loss of Efficacy: Current Situation in the USA and Europe, and Future Scenarios

2021 ◽  
Author(s):  
Anastasia Diakou ◽  
Roger K. Prichard
Keyword(s):  
Selection Pressure ◽  
Dirofilaria Immitis ◽  
Evolutionary Process ◽  
Resistance Development ◽  
Larval Stages ◽  
Resistance Selection ◽  
The Usa ◽  
Suppression Test ◽  
Resistant Strains ◽  
Geographical Point

Dirofilaria immitis infection is one of the most severe parasitic diseases of dogs. Prevention is achieved by the administration of drugs containing macrocyclic lactones (MLs). These products are very safe and highly effective, targeting the third and fourth larval stages (L3, L4) of the parasite. Until 2011, claims of ineffectiveness of MLs, reported as “Lack of Efficacy” (LOE), were generally attributed to owners’ non-compliance, or other reason for inadequate preventative coverage. There was solid argumentation that a resistance problem is not likely to occur because of i) the great extent of refugia, ii) the complexity of resistance development to MLs, and iii) the possible big number of genes involved in resistance selection. Nevertheless, today it is unequivocally proven that ML resistant D. immitis strains exist, at least in the Lower Mississippi region, USA. Accordingly, tools have been developed, to evaluate and confirm the susceptibility status of D. immitis strains. A simple, in-clinic, microfilariae suppression test, 14-28 days after ML administration, and a “decision tree” (algorithm), including compliance and preventatives’ purchase history, and testing gaps, may be applied for assessing any resistant nature of the parasite. On the molecular level, specific SNPs may be used as markers of ML resistance, offering a basis for validation of clinically suspected resistant strains. In Europe, no LOE/resistance claims have been reported so far, and the existing conditions (stray dogs, rich wildlife, majority of owned dogs not on preventive MLs treatment) do not favor selection pressure on the parasites. Considering the genetic basis of resistance and the epizootiological characteristics of D. immitis, ML resistance neither establishes easily nor spreads quickly, a fact confirmed by the current known dispersion of the problem, which is limited. Nevertheless, ML resistance may propagate from an initial geographical point, via animal and vector mobility, to other regions, while it can also emerge as an independent evolutionary process in a new area. For these reasons and considering the current chemoprophylaxis recommendations and increasing use of ML endectoparasiticides as a potential selection pressure, it is important to remain vigilant for timely detection of any ML LOE/resistance, in all continents where D. immitis is enzootic.

scholarly journals An Immuno-Genetic Hybrid Algorithm

2009 ◽  
Vol 4 (4) ◽  
pp. 374 ◽  
Author(s):  
Emad Nabil ◽  
Amr Badr ◽  
Ibrahim Farag
Keyword(s):  
Genetic Algorithms ◽  
Immune System ◽  
Hybrid Algorithm ◽  
Clonal Selection ◽  
Fuzzy Rule ◽  
Mutation Rates ◽  
Clonal Selection Algorithm ◽  
Artificial Immune ◽  
Binary Character ◽  
Rule System

The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before them.

Asymptotic convergence of genetic algorithms

1998 ◽  
Vol 30 (2) ◽  
pp. 521-550 ◽  
Author(s):  
Raphaël Cerf
Keyword(s):  
Genetic Algorithms ◽  
Population Size ◽  
Fitness Function ◽  
Evolutionary Process ◽  
Asymptotic Convergence ◽  
Homogeneous Case ◽  
Selection Scheme ◽  
Convergence Results ◽  
Asymptotic Dynamics ◽  
Simple Selection

We study a markovian evolutionary process which encompasses the classical simple genetic algorithm. This process is obtained by randomly perturbing a very simple selection scheme. Using the Freidlin-Wentzell theory, we carry out a precise study of the asymptotic dynamics of the process as the perturbations disappear. We show how a delicate interaction between the perturbations and the selection pressure may force the convergence toward the global maxima of the fitness function. We put forward the existence of a critical population size, above which this kind of convergence can be achieved. We compute upper bounds of this critical population size for several examples. We derive several conditions to ensure convergence in the homogeneous case and these provide the first mathematically well-founded convergence results for genetic algorithms.

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