Fixed Mutation Rate Research Articles

Sexual selection for males with beneficial mutations

Sexual selection is the process by which traits providing a mating advantage are favoured. Theoretical treatments of the evolution of sex by sexual selection propose that it operates by reducing the load of deleterious mutations. Here, we postulate instead that sexual selection primarily acts through females preferentially mating with males carrying beneficial mutations. We used simulation and analytical modelling to investigate the evolutionary dynamics of beneficial mutations in the presence of sexual selection. We found that female choice for males with beneficial mutations had a much greater impact on genetic quality than choice for males with low mutational load. We also relaxed the typical assumption of a fixed mutation rate. For deleterious mutations, mutation rate should always be minimized, but when rare beneficial mutations can occur, female choice for males with those rare beneficial mutations could overcome a decline in average fitness and allow an increase in mutation rate. We propose that sexual selection for beneficial mutations could overcome the ‘two-fold cost of sex’ much more readily than choice for males with low mutational load and may therefore be a more powerful explanation for the prevalence of sexual reproduction than the existing theory. If sexual selection results in higher fitness at higher mutation rates, and if the variability produced by mutation itself promotes sexual selection, then a feedback loop between these two factors could have had a decisive role in driving adaptation.

Mycobacterium tuberculosis progresses through two phases of latent infection in humans

Little is known about the physiology of latent Mycobacterium tuberculosis infection. We studied the mutational rates of 24 index tuberculosis (TB) cases and their latently infected household contacts who developed active TB up to 5.25 years later, as an indication of bacterial physiological state and possible generation times during latent TB infection in humans. Here we report that the rate of new mutations in the M. tuberculosis genome decline dramatically after two years of latent infection (two-sided p < 0.001, assuming an 18 h generation time equal to log phase M. tuberculosis, with latency period modeled as a continuous variable). Alternatively, assuming a fixed mutation rate, the generation time increases over the latency duration. Mutations indicative of oxidative stress do not increase with increasing latency duration suggesting a lack of host or bacterial derived mutational stress. These results suggest that M. tuberculosis enters a quiescent state during latency, decreasing the risk for mutational drug resistance and increasing generation time, but potentially increasing bacterial tolerance to drugs that target actively growing bacteria.

Self-Adaptation in Nonelitist Evolutionary Algorithms on Discrete Problems With Unknown Structure

A key challenge to make effective use of evolutionary algorithms (EAs) is to choose appropriate settings for their parameters. However, the appropriate parameter setting generally depends on the structure of the optimization problem, which is often unknown to the user. Nondeterministic parameter control mechanisms adjust parameters using information obtained from the evolutionary process. Self-adaptation—where parameter settings are encoded in the chromosomes of individuals and evolve through mutation and crossover—is a popular parameter control mechanism in evolutionary strategies. However, there is little theoretical evidence that self-adaptation is effective, and self-adaptation has largely been ignored by the discrete evolutionary computation community. Here, we show through a theoretical runtime analysis that a nonelitist, discrete EA which self-adapts its mutation rate not only outperforms EAs which use static mutation rates on $\mathrm{L{\scriptstyle EADING}O{\scriptstyle NES}}_{k}$ but also improves asymptotically on an EA using a state-of-the-art control mechanism. The structure of this problem depends on a parameter $k$ , which is a priori unknown to the algorithm, and which is needed to appropriately set a fixed mutation rate. The self-adaptive EA achieves the same asymptotic runtime as if this parameter was known to the algorithm beforehand, which is an asymptotic speedup for this problem compared to all other EAs previously studied. An experimental study of how the mutation-rates evolve show that they respond adequately to a diverse range of problem structures. These results suggest that self-adaptation should be adopted more broadly as a parameter control mechanism in discrete, nonelitist EAs.

ISLAND MIGRATION MODEL WITH PARALLEL MUTATION STRATEGIES FOR COMPUTING THE TRAVELING SALESMAN PROBLEM ON MULTICOMPUTER PLATFORM

This paper investigates the efficiency of a model of parallel genetic computation of the traveling salesman problem with circular periodic chromosomes migration. The parallel model is verified by MPI-based program implementation on a multicomputer platform. The correlation of the application and architectural spaces has been investigated by exploring the impact of the scalability of the application and the parallel machine size over the efficiency of the parallel system. Performance profiling, evaluation and analysis have been made for different numbers of cities and different sizes of the multicomputer platform. The paper also investigates the impact of the mutation strategy on the solution quality of coarse-grained parallel genetic algorithm with circular periodic migration for the traveling salesman problem. We propose an approach to improve the quality of solution by applying parallel variable mutation rates for the local evolutions in the concurrent processes. A series of experiments has been carried out with parallel fixed and variable mutation rates in order to estimate the efficiency of the suggested approach. The best quality solutions have been obtained for the strategy with parallel fixed mutation rates.

A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

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 novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

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.

Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

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.

Impacts of Genetic Algorithm Parameters on the Solution Performance for the Uniform Circular Antenna Array Pattern Synthesis Problem

In this paper, the uniform circular antenna array pattern synthesis problem is solved by means of the real coded genetic algorithm (GA). At the same time, the impacts of the mutation rate and the crossover position on the GAperformance are also investigated. For this purpose, a circular antenna array with uniformly spaced isotropic elements having identical excitation amplitudes is used as a model. Unlike the conventional GA (with fixed mutation rate and random crossover positions), typical GA implementations with variable mutation rate and restricted crossover position are considered for performance improvement. In conclusion, for the specific problem, decreasing mutation rate with negative derivative is observed to be outperforming the implementations with different mutation rate behaviors. Moreover, regarding the crossover technique, it is observed that imposing some restrictions on the crossover positions (rather than fully random position selection) yields better solutions.

Limitations of Existing Mutation Rate Heuristics and How a Rank GA Overcomes Them

Using a set of different search metrics and a set of model landscapes we theoretically and empirically study how ldquooptimalrdquo mutation rates for the simple genetic algorithm (SGA) depend not only on the fitness landscape, but also on population size and population state. We discuss the limitations of current mutation rate heuristics, showing that any fixed mutation rate can be expected to be suboptimal in terms of balancing exploration and exploitation. We then develop a mutation rate heuristic that offers a better balance by assigning different mutation rates to different subpopulations. When the mutation rate is assigned through a ranking of the population, according to fitness for example, we call the resulting algorithm a Rank GA. We show how this Rank GA overcomes the limitations of other heuristics on a set of model problems showing under what circumstances it might be expected to outperform a SGA with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">any</i> choice of mutation rate.

Genetic algorithms applied in BOPP film scheduling problems: minimizing total absolute deviation and setup times

The frequent changeovers in the production processes indicate the importance of setup time in many real-world manufacturing activities. The traditional approaches in dealing with setup times are that either to omit or to merge into the processing times so as to simplify the problems. These approaches could reduce the complexity of the problem, but often generated unrealistic outcomes because of the assumed conditions. This situation motivated us to consider sequence-dependent setup times in a real-world BOPP film scheduling problem. First, a setup time-based heuristic method was developed to generate the initial solutions for the genetic algorithms (GAs). Then, genetic algorithms with different mutation methods were applied. Extensive experimental results showed that the setup time-based heuristic method was relatively efficient. It was also found that a genetic algorithm with the variable mutation rate performed much effectively than one with the fixed mutation rate.

Evolution of evolvability via adaptation of mutation rates

We examine a simple form of the evolution of evolvability—the evolution of mutation rates—in a simple model system. The system is composed of many agents moving, reproducing, and dying in a two-dimensional resource-limited world. We first examine various macroscopic quantities (three types of genetic diversity, a measure of population fitness, and a measure of evolutionary activity) as a function of fixed mutation rates. The results suggest that (i) mutation rate is a control parameter that governs a transition between two qualitatively different phases of evolution, an ordered phase characterized by punctuated equilibria of diversity, and a disordered phase of characterized by noisy fluctuations around an equilibrium diversity, and (ii) the ability of evolution to create adaptive structure is maximized when the mutation rate is just below the transition between these two phases of evolution. We hypothesize that this transition occurs when the demands for evolutionary memory and evolutionary novelty are typically balanced. We next allow the mutation rate itself to evolve, and we observe that evolving mutation rates adapt to values at this transition. Furthermore, the mutation rates adapt up (or down) as the evolutionary demands for novelty (or memory) increase, thus supporting the balance hypothesis.

Numerical Study on Error Catastrophe of Adaptive Mutants and Their Stability

We study an error catastrophe of a self-replicating system, in which a number of bit strings with variable mutation rate compete to survive in a given rugged fitness landscape. The error catastrophe is known as a phase transition from an ordered state to a disordered state when a mutation rate increases. As a control parameter, we choose a selection pressure rather than a mutation rate. We find that our system shows the error catastrophe when the selection pressure decreases. An order parameter is introduced and the error catastrophe is studied for three types of mutant; (a) each bit has a fixed mutation rate, (b) each bit has a common variable mutation rate, and (c) each bit has a variable mutation rate. We also analyze stability of the system on the basis of the eigenvalue and the eigenvector of the transition matrix.

Possible role of natural selection in the formation of tandem-repetitive noncoding DNA.

A simulation model of sequence-dependent amplification, unequal crossing over and mutation is analyzed. This model predicts the spontaneous formation of tandem-repetitive patterns of noncoding DNA from arbitrary sequences for a wide range of parameter values. Natural selection is found to play an essential role in this self-organizing process. Natural selection which is modeled as a mechanism for controlling the length of a nucleotide string but not the sequence itself favors the formation of tandem-repetitive structures. Two measures of sequence heterogeneity, inter-repeat variability and repeat length, are analyzed in detail. For fixed mutation rate, both inter-repeat variability and repeat length are found to increase with decreasing rates of (unequal) crossing over. The results are compared with data on micro-, mini- and satellite DNAs. The properties of minisatellites and satellite DNAs resemble the simulated structures very closely. This suggests that unequal crossing over is a dominant long-range ordering force which keeps these arrays homogeneous even in regions of very low recombination rates, such as at satellite DNA loci. Our analysis also indicates that in regions of low rates of (unequal) crossing over, inter-repeat variability is maintained at a low level at the expense of much larger repeat units (multimeric repeats), which are characteristic of satellite DNA. In contrast, the microsatellite data do not fit the proposed model well, suggesting that unequal crossing over does not act on these very short tandem arrays.

Towards a general theory of adaptive walks on rugged landscapes

Adaptive evolution, to a large extent, is a complex combinatorial optimization process. In this article we take beginning steps towards developing a general theory of adaptive "walks" via fitter variants in such optimization processes. We introduce the basic idea of a space of entities, each a 1-mutant neighbor of many other entities in the space, and the idea of a fitness ascribed to each entity. Adaptive walks proceed from an initial entity, via fitter neighbors, to locally or globally optimal entities that are fitter than their neighbors. We develop a general theory for the number of local optima, lengths of adaptive walks, and the number of alternative local optima accessible from any given initial entity, for the baseline case of an uncorrelated fitness landscape. Most fitness landscapes are correlated, however. Therefore we develop parts of a universal theory of adaptation on correlated landscapes by adaptive processes that have sufficient numbers of mutations per individual to "jump beyond" the correlation lengths in the underlying landscape. In addition, we explore the statistical character of adaptive walks in two independent complex combinatorial optimization problems, that of evolving a specific cell type in model genetic networks, and that of finding good solutions to the traveling salesman problem. Surprisingly, both show similar statistical features, encouraging the hope that a general theory for adaptive walks on correlated and uncorrelated landscapes can be found. In the final section we explore two limits to the efficacy of selection. The first is new, and surprising: for a wide class of systems, as the complexity of the entities under selection increases, the local optima that are attainable fall progressively closer to the mean properties of the underlying space of entities. This may imply that complex biological systems, such as genetic regulatory systems, are "close" to the mean properties of the ensemble of genomic regulatory systems explored by evolution. The second limit shows that with increasing complexity and a fixed mutation rate, selection often becomes unable to pull an adapting population to those local optima to which connected adaptive walks via fitter variants exist. These beginning steps in theory development are applied to maturation of the immune response, and to the problem of radiation and stasis. Despite the limitations of the adaptive landscape metaphor, we believe that further development along the lines begun here will prove useful.