Journal articles: 'Genetic Fitness'

1

Montgomery, Hugh, and Latif Safari. "Genetic Basis of Physical Fitness." Annual Review of Anthropology 36, no. 1 (September 2007): 391–405. http://dx.doi.org/10.1146/annurev.anthro.36.081406.094333.

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Bouchard, C., T. Rankinen, L. Perusse, F. Booth, and S. Britton. "GENETIC DIFFERENCES, FITNESS AND PERFORMANCE." Medicine & Science in Sports & Exercise 34, no. 5 (May 2002): S46. http://dx.doi.org/10.1097/00005768-200205001-00248.

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Zhai, W., P. Kelly, and W. B. Gong. "Genetic algorithms with noisy fitness." Mathematical and Computer Modelling 23, no. 11-12 (June 1996): 131–42. http://dx.doi.org/10.1016/0895-7177(96)00068-4.

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Charlesworth, Brian. "Causes of natural variation in fitness: Evidence from studies of Drosophila populations." Proceedings of the National Academy of Sciences 112, no. 6 (January 8, 2015): 1662–69. http://dx.doi.org/10.1073/pnas.1423275112.

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DNA sequencing has revealed high levels of variability within most species. Statistical methods based on population genetics theory have been applied to the resulting data and suggest that most mutations affecting functionally important sequences are deleterious but subject to very weak selection. Quantitative genetic studies have provided information on the extent of genetic variation within populations in traits related to fitness and the rate at which variability in these traits arises by mutation. This paper attempts to combine the available information from applications of the two approaches to populations of the fruitfly Drosophila in order to estimate some important parameters of genetic variation, using a simple population genetics model of mutational effects on fitness components. Analyses based on this model suggest the existence of a class of mutations with much larger fitness effects than those inferred from sequence variability and that contribute most of the standing variation in fitness within a population caused by the input of mildly deleterious mutations. However, deleterious mutations explain only part of this standing variation, and other processes such as balancing selection appear to make a large contribution to genetic variation in fitness components in Drosophila.

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O’Brien, Anna M., Chandra N. Jack, Maren L. Friesen, and Megan E. Frederickson. "Whose trait is it anyways? Coevolution of joint phenotypes and genetic architecture in mutualisms." Proceedings of the Royal Society B: Biological Sciences 288, no. 1942 (January 13, 2021): 20202483. http://dx.doi.org/10.1098/rspb.2020.2483.

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Evolutionary biologists typically envision a trait’s genetic basis and fitness effects occurring within a single species. However, traits can be determined by and have fitness consequences for interacting species, thus evolving in multiple genomes. This is especially likely in mutualisms, where species exchange fitness benefits and can associate over long periods of time. Partners may experience evolutionary conflict over the value of a multi-genomic trait, but such conflicts may be ameliorated by mutualism’s positive fitness feedbacks. Here, we develop a simulation model of a host–microbe mutualism to explore the evolution of a multi-genomic trait. Coevolutionary outcomes depend on whether hosts and microbes have similar or different optimal trait values, strengths of selection and fitness feedbacks. We show that genome-wide association studies can map joint traits to loci in multiple genomes and describe how fitness conflict and fitness feedback generate different multi-genomic architectures with distinct signals around segregating loci. Partner fitnesses can be positively correlated even when partners are in conflict over the value of a multi-genomic trait, and conflict can generate strong mutualistic dependency. While fitness alignment facilitates rapid adaptation to a new optimum, conflict maintains genetic variation and evolvability, with implications for applied microbiome science.

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GUO, Guang-song, Dun-wei GONG, Guo-sheng HAO, and Yong ZHANG. "Interactive Genetic Algorithms with Fitness Adjustment." Journal of China University of Mining and Technology 16, no. 4 (December 2006): 480–84. http://dx.doi.org/10.1016/s1006-1266(07)60052-2.

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Takahashi, Yuma, Ryoya Tanaka, Daisuke Yamamoto, Suzuki Noriyuki, and Masakado Kawata. "Balanced genetic diversity improves population fitness." Proceedings of the Royal Society B: Biological Sciences 285, no. 1871 (January 17, 2018): 20172045. http://dx.doi.org/10.1098/rspb.2017.2045.

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Although genetic diversity within a population is suggested to improve population-level fitness and productivity, the existence of these effects is controversial because empirical evidence for an ecological effect of genetic diversity and the underlying mechanisms is scarce and incomplete. Here, we show that the natural single-gene behavioural polymorphism ( Rover and sitter ) in Drosophila melanogaster has a positive effect on population fitness. Our simple numerical model predicted that the fitness of a polymorphic population would be higher than that expected with two monomorphic populations, but only under balancing selection. Moreover, this positive diversity effect of genetic polymorphism was attributable to a complementarity effect, rather than to a selection effect. Our empirical tests using the behavioural polymorphism in D. melanogaster clearly supported the model predictions. These results provide direct evidence for an ecological effect of genetic diversity on population fitness and its condition dependence.

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Rankinen, Tuomo. "Genetic Influences on Fitness and Activity." Medicine & Science in Sports & Exercise 40, Supplement (May 2008): 65. http://dx.doi.org/10.1249/01.mss.0000321314.29517.2e.

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Vose, Michael D., and Alden H. Wright. "Simple Genetic Algorithms with Linear Fitness." Evolutionary Computation 2, no. 4 (December 1994): 347–68. http://dx.doi.org/10.1162/evco.1994.2.4.347.

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A general form of stochastic search is described (random heuristic search), and some of its general properties are proved. This provides a framework in which the simple genetic algorithm (SGA) is a special case. The framework is used to illuminate relationships between seemingly different probabilistic perspectives of SGA behavior. Next, the SGA is formalized as an instance of random heuristic search. The formalization then used to show expected population fitness is a Lyapunov function in the infinite population model when mutation is zero and fitness is linear. In particular, the infinite population algorithm must converge, and average population fitness increases from one generation to the next. The consequence for a finite population SGA is that the expected population fitness increases from one generation to the next. Moreover, the only stable fixed point of the expected next population operator corresponds to the population consisting entirely of the optimal string. This result is then extended by way of a perturbation argument to allow nonzero mutation.

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Reed, David H., and Richard Frankham. "Correlation between Fitness and Genetic Diversity." Conservation Biology 17, no. 1 (February 2003): 230–37. http://dx.doi.org/10.1046/j.1523-1739.2003.01236.x.

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11

Srinivas, M., and L. M. Patnaik. "Genetic search: analysis using fitness moments." IEEE Transactions on Knowledge and Data Engineering 8, no. 1 (1996): 120–33. http://dx.doi.org/10.1109/69.485641.

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Huang, Jianjun, and Weixin Xie. "Genetic algorithm with fuzzy fitness evaluation." Journal of Electronics (China) 15, no. 3 (July 1998): 254–58. http://dx.doi.org/10.1007/s11767-998-0037-4.

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Elena, Santiago F., Fernando González-Candelas, Isabel S. Novella, Elizabeth A. Duarte, David K. Clarke, Esteban Domingo, John J. Holland, and Andrés Moya. "Evolution of Fitness in Experimental Populations of Vesicular Stomatitis Virus." Genetics 142, no. 3 (March 1, 1996): 673–79. http://dx.doi.org/10.1093/genetics/142.3.673.

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Abstract The evolution of fitness in experimental clonal populations of vesicular stomatitis virus (VSV) has been compared under different genetic (fitness of initial clone) and demographic (population dynamics) regimes. In spite of the high genetic heterogeneity among replicates within experiments, there is a clear effect of population dynamics on the evolution of fitness. Those populations that went through strong periodic bottlenecks showed a decreased fitness in competition experiments with wild type. Conversely, mutant populations that were transferred under the dynamics of continuous population expansions increased their fitness when compared with the same wild type. The magnitude of the observed effect depended on the fitness of the original viral clone. Thus, high fitness clones showed a larger reduction in fitness than low fitness clones under dynamics with included periodic bottleneck. In contrast, the gain in fitness was larger the lower the initial fitness of the viral clone. The quantitative genetic analysis of the trait “fitness” in the resulting populations shows that genetic variation for the trait is positively correlated with the magnitude of the change in the same trait. The results are interpreted in terms of the operation of Muller's ratchet and genetic drift as opposed to the appearance of beneficial mutations.

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Dykhuizen, Daniel E., Antony M. Dean, and Daniel L. Hartl. "Metabolic Flux and Fitness." Genetics 115, no. 1 (January 1, 1987): 25–31. http://dx.doi.org/10.1093/genetics/115.1.25.

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ABSTRACT Studies of Escherichia coli under competition for lactose in chemostat cultures have been used to determine the selective effects of variation in the level of the β-galactoside permease and the β-galactosidase enzyme. The results determine the adaptive topography of these gene products relative to growth in limiting lactose and enable predictions concerning the selective effects of genetic variants found in natural populations. In the terms of metabolic control theory, the β-galactosidase enzyme at wild-type-induced levels has a small control coefficient with respect to fitness (C = 0.018), and hence genetic variants resulting in minor changes in enzyme activity have disproportionately small effects on fitness. However, the apparent control coefficient of the β-galactoside permease at wild-type-induced levels is large (C = 0.551), and hence even minor changes in activity affect fitness. Therefore, we predict that genetic polymorphisms in the lacZ gene are subject to less effective selection in natural populations than are those in the lacY gene. The β-galactoside permease is also less efficient than might be expected, and possible forces resulting in selection for an intermediate optimum level of permease activity are considered. The selective forces that maintain the lactose operon in a regulated state in natural populations are also discussed.

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Hale, PT. "Genetic effects of kangaroo harvesting." Australian Mammalogy 26, no. 1 (2004): 75. http://dx.doi.org/10.1071/am04075.

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There is concern that the commercial harvest of kangaroos (Macropus spp.) is affecting species fitness and evolutionary potential because the harvest selects for larger individuals, particularly males. This paper reviews the likely effect of selective harvesting on specific traits associated with fitness, including size, and on adaptive genotypes through generalised loss of gene diversity. Heritability for traits associated with fitness is low generally. The intensity of selection imposed by harvesting is low for several reasons: the geographic size of genetic populations is much larger than the harvest localities, which are therefore not closed but open with immigration acting to correct any change in allele frequencies through harvesting; the harvest targets kangaroos above a threshold weight that includes all adult males, not the largest males specifically; larger, older males may not confer significant fitness benefits on offspring; fitness traits are inherited through both sexes while males are targeted predominantly; populations are not at a selective equilibrium because food availability fluctuates, and the fittest is unlikely to be the largest. Comparisons of harvested and unharvested populations do not show any loss of gene diversity as a result of harvesting. The likelihood of a long-term genetic impact of kangaroo harvesting as currently practiced is negligible.

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16

Gerasymenko, Volodymyr. "The Model of Fitness in a Heterogeneous Environment on Reaction Norms." Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences. 71, no. 4 (August 1, 2017): 303–6. http://dx.doi.org/10.1515/prolas-2017-0051.

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Abstract The understanding of genetic mechanisms of natural selection to a large extent became possible as a result of modelling research carried out in the fields of evolutionary and population genetics. However, genetic models cannot be considered exhaustive in the description of natural selection because a phenotype of individual, environment and fitness remains beyond their framework. Consequently, the value of fitness is not derived from the algorithmic model but arbitrarily assigned by a researcher to genotypes in their genetic formula. This work proposes a model of genotype fitness in heterogeneous environments on reaction norms in connection with the genetic structure of the population. Two equations represent the model. The first is an incomplete second order polynomial that describes the dependence of fitness on the phenotype of an adaptive trait and environmental conditions. The second is a linear equation of the reaction norm of the adaptive trait that determines its phenotype in specific environmental conditions. According to the model algorithms, rating of fitness in the population, and, consequently, their probability of selection, is determined by phenotype optimality and their norm of reaction in certain environmental conditions.

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Korona, Ryszard. "Genetic Divergence and Fitness Convergence Under Uniform Selection in Experimental Populations of Bacteria." Genetics 143, no. 2 (June 1, 1996): 637–44. http://dx.doi.org/10.1093/genetics/143.2.637.

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Abstract Replicate populations of bacteria were propagated for 1000 generations in the laboratory. The growth substrate was periodically renewed, so that during most generations (cell doublings) it was not limiting. The final clones demonstrated about a 40% fitness increase when competed against their common ancestor. This increase was uniform both among and within populations despite extensive differentiation in correlated traits: cell size, resistance to starvation and dry mass of culture. It is suggested that genetic diversity developed because selection promoted any changes directing cell activity toward a higher maximum growth rate. Evolution of this trait halted at a similar level when some basic constraints on bacterial metabolism were met. The selective values of emerging mutations must have depended on the genetic background. They would be beneficial early in evolution but ineffective near the limit of adaptation. This hypothesis was tested for one mutation that affected both fitness and colony morphology. In some clones it was the first adaptive mutation and provided a third of the total fitness increase, but it was not assimilated by the clones that reached the adaptive ceiling in some other way. Near the limit of adaptation, epistasis levels off the fitnesses of genetically variable clones.

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McFarlane, S. Eryn, Jamieson C. Gorrell, David W. Coltman, Murray M. Humphries, Stan Boutin, and Andrew G. McAdam. "The nature of nurture in a wild mammal's fitness." Proceedings of the Royal Society B: Biological Sciences 282, no. 1806 (May 7, 2015): 20142422. http://dx.doi.org/10.1098/rspb.2014.2422.

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Genetic variation in fitness is required for the adaptive evolution of any trait but natural selection is thought to erode genetic variance in fitness. This paradox has motivated the search for mechanisms that might maintain a population's adaptive potential. Mothers make many contributions to the attributes of their developing offspring and these maternal effects can influence responses to natural selection if maternal effects are themselves heritable. Maternal genetic effects (MGEs) on fitness might, therefore, represent an underappreciated source of adaptive potential in wild populations. Here we used two decades of data from a pedigreed wild population of North American red squirrels to show that MGEs on offspring fitness increased the population's evolvability by over two orders of magnitude relative to expectations from direct genetic effects alone. MGEs are predicted to maintain more variation than direct genetic effects in the face of selection, but we also found evidence of maternal effect trade-offs. Mothers that raised high-fitness offspring in one environment raised low-fitness offspring in another environment. Such a fitness trade-off is expected to maintain maternal genetic variation in fitness, which provided additional capacity for adaptive evolution beyond that provided by direct genetic effects on fitness.

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Morrissey, Michael B., and Timothée Bonnet. "Analogues of the fundamental and secondary theorems of selection, assuming a log-normal distribution of expected fitness." Journal of Heredity 110, no. 4 (June 2019): 396–402. http://dx.doi.org/10.1093/jhered/esz020.

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Abstract It is increasingly common for studies of evolution in natural populations to infer the quantitative genetic basis of fitness (e.g., the additive genetic variance for relative fitness), and of relationships between traits and fitness (e.g., the additive genetic covariance of traits with relative fitness). There is a certain amount of tension between the theory that justifies estimating these quantities, and methodological considerations relevant to their empirical estimation. In particular, the additive genetic variances and covariances involving relative fitness are justified by the fundamental and secondary theorems of selection, which pertain to relative fitness on the scale that it is expressed. However, naturally-occurring fitness distributions lend themselves to analysis with generalized linear mixed models (GLMMs), which conduct analysis on a different scale, typically on the scale of the logarithm of expected values, from which fitness is expressed. This note presents relations between evolutionary change in traits, and the rate of adaptation in fitness, and log quantitative genetic parameters of fitness, potentially reducing the discord between theoretical and methodological considerations to the operationalization of the secondary and fundamental theorems of selection.

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IGLESIAS, M. T., V. S. PEÑARANDA, C. VIDAL, and A. VERSCHOREN. "HIGHER EPISTASIS IN GENETIC ALGORITHMS." Bulletin of the Australian Mathematical Society 77, no. 2 (April 2008): 225–43. http://dx.doi.org/10.1017/s0004972708000233.

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AbstractWe study thek-epistasis of a fitness function over a search space. This concept is a natural generalization of that of epistasis, previously considered by Davidor, Suys and Verschoren and Van Hove and Verschoren [Y. Davidor, in:Foundations of genetic algorithms, Vol. 1, (1991), pp. 23–25; D. Suys and A. Verschoren, ‘Proc Int. Conf. on Intelligent Technologies in Human-Related Sciences (ITHURS’96), Vol. II (1996), pp. 251–258; H. Van Hove and A. Verschoren,Comput. Artificial Intell.14(1994), 271–277], for example. We completely characterize fitness functions whosek-epistasis is minimal: these are exactly the functions of orderk. We also obtain an upper bound for thek-epistasis of nonnegative fitness functions.

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Kumar, V. Sivaram, M. R. Thansekhar, and R. Saravanan. "A New Multi Objective Genetic Algorithm: Fitness Aggregated Genetic Algorithm (FAGA) for Vehicle Routing Problem." Advanced Materials Research 984-985 (July 2014): 1261–68. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.1261.

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This paper presents multi objective vehicle routing problem in which the total distance travelled by the vehicles and total number of vehicles used are minimized. In general, fitness assignment procedure, as one of the important operators, influences the effectiveness of multi objective genetic algorithms. In this paper genetic algorithm with different fitness assignment approach and specialized crossover called Fitness Aggregated Genetic Algorithm (FAGA) is introduced for solving the problem. The suggested algorithm is investigated on large number of popular benchmarks for vehicle routing problem. It is observed from the results that the suggested new algorithm is very effective and the solutions are competitive with the best known results.

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House, Amanda, and Arvin Agah. "Autonomous Evolution of Digital Art Using Genetic Algorithms." Journal of Intelligent Systems 25, no. 3 (July 1, 2016): 319–33. http://dx.doi.org/10.1515/jisys-2014-0173.

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AbstractThis paper applies a genetic algorithm (GA) to the autonomous evolution of digital art, eliminating the need for a human in the loop. Creative applications of GAs face the challenge of producing art or music to fit a wide range of human tastes. One approach is to use a human in the loop to determine the fitness function in order to direct the selection and evolution. Another approach, which this paper explores, is to define an objective fitness function to automate evolution without the need for human input. In this paper, several features of digital art are identified and used as the basis for fitness functions. The resulting images are recognizable for the intended evolution of the fitness function used. This indicates the potential of an approach to create more robust algorithmic fitness functions capable of evolving creative applications autonomously.

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FAN, Xiao-qin, and Neng-fa HU. "Partheno-genetic algorithm based on dual fitness." Journal of Computer Applications 29, no. 7 (July 30, 2009): 1887–89. http://dx.doi.org/10.3724/sp.j.1087.2009.01887.

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Klusáček, Jan, and Václav Jirsík. "Comparing Fitness Functions for Genetic Feature Transformation." IFAC-PapersOnLine 49, no. 25 (2016): 299–304. http://dx.doi.org/10.1016/j.ifacol.2016.12.053.

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Lim, Gregory B. "Fitness ameliorates genetic risk of heart disease." Nature Reviews Cardiology 15, no. 7 (April 26, 2018): 380. http://dx.doi.org/10.1038/s41569-018-0019-7.

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Agrawal, A. F. "Genetic loads under fitness-dependent mutation rates." Journal of Evolutionary Biology 15, no. 6 (October 25, 2002): 1004–10. http://dx.doi.org/10.1046/j.1420-9101.2002.00464.x.

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Schutte, Nienke, Meike Bartels, Ineke Nederend, and Eco de Geus. "Genetic Effects on Adolescent Physical Fitness Components." Medicine & Science in Sports & Exercise 46 (May 2014): 597. http://dx.doi.org/10.1249/01.mss.0000495262.04810.53.

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Trenkmann, Michelle. "Putting genetic variants to a fitness test." Nature Reviews Genetics 19, no. 11 (October 2, 2018): 667. http://dx.doi.org/10.1038/s41576-018-0056-4.

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KREINOVICH, VLADIK, CHRIS QUINTANA, and OLAC FUENTES. "GENETIC ALGORITHMS: WHAT FITNESS SCALING IS OPTIMAL?" Cybernetics and Systems 24, no. 1 (January 1993): 9–26. http://dx.doi.org/10.1080/01969729308961696.

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Gong, Dun-wei, Jie Yuan, and Xiao-yan Sun. "Interactive genetic algorithms with individual’s fuzzy fitness." Computers in Human Behavior 27, no. 5 (September 2011): 1482–92. http://dx.doi.org/10.1016/j.chb.2010.10.012.

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Ragalo, Anisa, and Nelishia Pillay. "Evolving dynamic fitness measures for genetic programming." Expert Systems with Applications 109 (November 2018): 162–87. http://dx.doi.org/10.1016/j.eswa.2018.03.060.

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Meuwissen, T. H. E., J. P. Gibson, and M. Quinton. "Genetic improvement of production while maintaining fitness." Theoretical and Applied Genetics 90, no. 5 (April 1995): 627–35. http://dx.doi.org/10.1007/bf00222126.

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SANTOS, EUNICE E., and EUGENE SANTOS. "EFFECTIVE AND EFFICIENT CACHING IN GENETIC ALGORITHMS." International Journal on Artificial Intelligence Tools 10, no. 01n02 (March 2001): 273–301. http://dx.doi.org/10.1142/s0218213001000520.

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Hard discrete optimization problems using randomized methods such as genetic algorithms require large numbers of samples from the solution space. Each candidate sample/solution must be evaluated using the target fitness/energy function being optimized. Such fitness computations are a bottleneck in sampling methods such as genetic algorithms. We observe that the caching of partial results from these fitness computations can reduce this bottleneck. We provide a rigorous analysis of the run-times of GAs with and without caching. By representing fitness functions as classic Divide and Conquer algorithms, we provide a formal model to predict the efficiency of caching GAs vs. non-caching GAs. Finally, we explore the domain of protein folding with GAs and demonstrate that caching can significantly reduce expected run-times through both theoretical and extensive empirical analyses.

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Han, Xiaosong, Yanchun Liang, Zhengguang Li, Gaoyang Li, Xiaozhou Wu, Binghong Wang, Guozhong Zhao, and Chunguo Wu. "An Efficient Genetic Algorithm for Optimization Problems with Time-Consuming Fitness Evaluation." International Journal of Computational Methods 12, no. 01 (January 23, 2015): 1350106. http://dx.doi.org/10.1142/s0219876213501065.

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In classical genetic algorithm, fitness evaluations are often very expensive or highly time-consuming, especially for some engineering optimization problems. We present an efficient genetic algorithm (GA) by combining clustering methods with an empirical fitness estimating formula. The new individuals are clustered at first, and then only the cluster representatives are really evaluated by its original time-consuming fitness computing processes, and other individuals undergo high efficient fitness evaluating processes by using the empirical fitness estimating formula. To further improve the accuracy of fitness estimations, we present a schema discovery strategy by extracting the common encoding characters from both high-fitness individual group and low-fitness individual group, and then adjust the estimated fitness for each individual based on the matching with the discovered schema. Experiments show that the schema discovery strategy contributes remarkably to the accuracy of fitness estimation. Numerical experiments of some well-known benchmark problems and a practical engineering problem demonstrate that the proposed method could improve the efficiency by over 30% in terms of the times of real fitness evaluations at the similar optimization accuracy of classical genetic algorithm.

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Hendry, Andrew P., Daniel J. Schoen, Matthew E. Wolak, and Jane M. Reid. "The Contemporary Evolution of Fitness." Annual Review of Ecology, Evolution, and Systematics 49, no. 1 (November 2, 2018): 457–76. http://dx.doi.org/10.1146/annurev-ecolsys-110617-062358.

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The rate of evolution of population mean fitness informs how selection acting in contemporary populations can counteract environmental change and genetic degradation (mutation, gene flow, drift, recombination). This rate influences population increases (e.g., range expansion), population stability (e.g., cryptic eco-evolutionary dynamics), and population recovery (i.e., evolutionary rescue). We review approaches for estimating such rates, especially in wild populations. We then review empirical estimates derived from two approaches: mutation accumulation (MA) and additive genetic variance in fitness (IAw). MA studies inform how selection counters genetic degradation arising from deleterious mutations, typically generating estimates of <1% per generation. IAw studies provide an integrated prediction of proportional change per generation, nearly always generating estimates of <20% and, more typically, <10%. Overall, considerable, but not unlimited, evolutionary potential exists in populations facing detrimental environmental or genetic change. However, further studies with diverse methods and species are required for more robust and general insights.

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Dean, A. M. "Selection and neutrality in lactose operons of Escherichia coli." Genetics 123, no. 3 (November 1, 1989): 441–54. http://dx.doi.org/10.1093/genetics/123.3.441.

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Abstract The kinetics of the permeases and beta-galactosidases of six lactose operons which had been transduced into a common genetic background from natural isolates of Escherichia coli were investigated. The fitnesses conferred by the operons were determined using chemostat competition experiments in which lactose was the sole growth-limiting factor. The cell wall is demonstrated to impose a resistance to the diffusion of galactosides at low substrate concentrations. A steady state model of the flux of lactose through the metabolic pathway (diffusion, uptake and hydrolysis) is shown to be proportional to fitness. This metabolic model is used to explain why an approximately twofold range in activity among the permease alleles confers a 13% range in fitness, whereas a similar range in activity among alleles of the beta-galactosidase confers a 0.5% range in fitness. This metabolic model implies that selection need not be maximized when a resource is scarce.

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Alphey, Nina, and Michael B. Bonsall. "Interplay of population genetics and dynamics in the genetic control of mosquitoes." Journal of The Royal Society Interface 11, no. 93 (April 6, 2014): 20131071. http://dx.doi.org/10.1098/rsif.2013.1071.

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Some proposed genetics-based vector control methods aim to suppress or eliminate a mosquito population in a similar manner to the sterile insect technique. One approach under development in Anopheles mosquitoes uses homing endonuclease genes (HEGs)—selfish genetic elements (inherited at greater than Mendelian rate) that can spread rapidly through a population even if they reduce fitness. HEGs have potential to drive introduced traits through a population without large-scale sustained releases. The population genetics of HEG-based systems has been established using discrete-time mathematical models. However, several ecologically important aspects remain unexplored. We formulate a new continuous-time (overlapping generations) combined population dynamic and genetic model and apply it to a HEG that targets and knocks out a gene that is important for survival. We explore the effects of density dependence ranging from undercompensating to overcompensating larval competition, occurring before or after HEG fitness effects, and consider differences in competitive effect between genotypes (wild-type, heterozygotes and HEG homozygotes). We show that population outcomes—elimination, suppression or loss of the HEG—depend crucially on the interaction between these ecological aspects and genetics, and explain how the HEG fitness properties, the homing rate (drive) and the insect's life-history parameters influence those outcomes.

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Mackay, Trudy F. C. "A quantitative genetic analysis of fitness and its components in Drosophila melanogaster." Genetical Research 47, no. 1 (February 1986): 59–70. http://dx.doi.org/10.1017/s0016672300024526.

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SummaryForty-one third chromosomes extracted from a natural population of Drosophila melanogaster were assessed for net fitness and for the quantitative characters viability, net fertility, female productivity, male weight, abdominal bristle number, and sternopleural bristle number. Net homozygous and heterozygous fitness of the third chromosomes was estimated by competition against a marked balancer third chromosome. Average fitness of the homozygous lines relative to wild-type heterozygotes was 0·13, indicating substantial inbreeding depression for net fitness. All significant correlations of quantitative characters with fitness and with each other were high and positive. Homozygous fitness is strongly correlated with net fertility, viability, and female productivity, moderately associated with male weight, and not significantly associated with bristle traits. The combination of metric traits which best predicts homozygous fitness is the simple multiple of viability and female productivity. Heterozygous fitness is not correlated with homozygous fitness; furthermore, the relative contribution of metric traits to fitness in a heterozygous population is likely to be different from that deduced from homozygous lines. These observations are consistent with a model of genetic variation for fitness in natural populations caused by segregation of rare deleterious recessive alleles.

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CHARLESWORTH, BRIAN, and DEBORAH CHARLESWORTH. "The genetic basis of inbreeding depression." Genetical Research 74, no. 3 (December 1999): 329–40. http://dx.doi.org/10.1017/s0016672399004152.

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Data on the effects of inbreeding on fitness components are reviewed in the light of population genetic models of the possible genetic causes of inbreeding depression. Deleterious mutations probably play a major role in causing inbreeding depression. Putting together the different kinds of quantitative genetic data, it is difficult to account for the very large effects of inbreeding on fitness in Drosophila and outcrossing plants without a significant contribution from variability maintained by selection. Overdominant effects of alleles on fitness components seem not to be important in most cases. Recessive or partially recessive deleterious effects of alleles, some maintained by mutation pressure and some by balancing selection, thus seem to be the most important source of inbreeding depression. Possible experimental approaches to resolving outstanding questions are discussed.

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Kassen, Rees, and Graham Bell. "THE ECOLOGY AND GENETICS OF FITNESS IN CHLAMYDOMONAS. X. THERELATIONSHIP BETWEEN GENETIC CORRELATION AND GENETIC DISTANCE." Evolution 54, no. 2 (2000): 425. http://dx.doi.org/10.1554/0014-3820(2000)054[0425:teagof]2.0.co;2.

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Rao, A. C. "A Genetic Algorithm for Topological Characteristics of Kinematic Chains." Journal of Mechanical Design 122, no. 2 (January 1, 2000): 228–31. http://dx.doi.org/10.1115/1.533569.

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A genetic algorithm for testing isomorphism among kinematic chains and to select the best frame and input links is presented. The computational effort involved is minimum and the method is unique as it satisfies both the necessary and sufficient requirements. Fitness of a binary string corresponding to a link is indicative of its design parameters. Consequently the fitness of a chain indicates the number of design parameters active in motion generation. Chains are compared for function generation on the basis of the ‘fitness’ of first generation and second generation ‘fitness,’ etc., in that order. [S1050-0472(00)00801-1]

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Korona, Ryszard. "Unpredictable Fitness Transitions Between Haploid and Diploid Strains of the Genetically Loaded Yeast Saccharomyces cerevisiae." Genetics 151, no. 1 (January 1, 1999): 77–85. http://dx.doi.org/10.1093/genetics/151.1.77.

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Abstract Mutator strains of yeast were used to accumulate random point mutations. Most of the observed changes in fitness were negative and relatively small, although major decreases and increases were also present. The average fitness of haploid strains was lowered by ∼25% due to the accumulated genetic load. The impact of the load remained basically unchanged when a homozygous diploid was compared with the haploid from which it was derived. In other experiments a heterozygous diploid was compared with the two different loaded haploids from which it was obtained. The fitness of such a loaded diploid was much less reduced and did not correlate with the average fitness of the two haploids. There was a fitness correlation, however, when genetically related heterozygous diploids were compared, indicating that the fitness effects of the new alleles were not entirely lost in the heterozygotes. It is argued here that to explain the observed pattern of fitness transitions it is necessary to invoke nonadditive genetic interactions that go beyond the uniform masking effect of wild-type alleles. Thus, the results gathered with haploids and homozygotes should be extrapolated to heterozygotes with caution when multiple loci contribute to the genetic load.

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Dyble, M., A. Gardner, L. Vinicius, and A. B. Migliano. "Inclusive fitness for in-laws." Biology Letters 14, no. 10 (October 2018): 20180515. http://dx.doi.org/10.1098/rsbl.2018.0515.

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Cooperation among kin is common across the natural world and can be explained in terms of inclusive fitness theory, which holds that individuals can derive indirect fitness benefits from aiding genetically related individuals. However, human kinship includes not only genetic kin but also kin by marriage: our affines (in-laws) and spouses. Can cooperation between these genetically unrelated kin be reconciled with inclusive fitness theory? Here, we argue that although affinal kin and spouses do not necessarily share genetic ancestry, they may have shared genetic interests in future reproduction and, as such, can derive indirect fitness benefits though cooperating. We use standard inclusive fitness theory to derive a coefficient of shared reproductive interest ( s ) that predicts altruistic investment both in genetic kin and in spouses and affines. Specifically, a behaviour that reduces the fitness of the actor by c and increases the fitness of the recipient by b will be favoured by natural selection when sb > c . We suggest that the coefficient of shared reproductive interest may provide a valuable tool for understanding not only the evolution of human kinship but also cooperation and conflict across the natural world more generally.

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Nagylaki, T. "The evolution of multilocus systems under weak selection." Genetics 134, no. 2 (June 1, 1993): 627–47. http://dx.doi.org/10.1093/genetics/134.2.627.

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Abstract The evolution of multilocus systems under weak selection is investigated. Generations are discrete and nonoverlapping; the monoecious population mates at random. The number of multi-allelic loci, the linkage map, dominance, and epistasis are arbitrary. The genotypic fitnesses may depend on the gametic frequencies and time. The results hold for s < cmin, where s and cmin denote the selection intensity and the smallest two-locus recombination frequency, respectively. After an evolutionarily short time of t1 approximately (ln s)/ln(1 - cmin) generations, all the multilocus linkage disequilibria are of the order of s [i.e., O(s) as s-->0], and then the population evolves approximately as if it were in linkage equilibrium, the error in the gametic frequencies being O(s). Suppose the explicit time dependence (if any) of the genotypic fitnesses is O(s2). Then after a time t2 approximately 2t1, the linkage disequilibria are nearly constant, their rate of change being O(s2). Furthermore, with an error of O(s2), each linkage disequilibrium is proportional to the corresponding epistatic deviation for the interaction of additive effects on fitness. If the genotypic fitnesses change no faster than at the rate O(s3), then the single-generation change in the mean fitness is delta W = W-1Vg+O(s3), where Vg designates the genic (or additive genetic) variance in fitness. The mean of a character with genotypic values whose single-generation change does not exceed O(s2) evolves at the rate delta Z = W-1Cg+O(s2), where Cg represents the genic covariance of the character and fitness (i.e., the covariance of the average effect on the character and the average excess for fitness of every allele that affects the character). Thus, after a short time t2, the absolute error in the fundamental and secondary theorems of natural selection is small, though the relative error may be large.

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Gardner, Michael P., Kevin Fowler, Nicholas H. Barton, and Linda Partridge. "Genetic Variation for Total Fitness in Drosophila melanogaster." Genetics 169, no. 3 (November 15, 2004): 1553–71. http://dx.doi.org/10.1534/genetics.104.032367.

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Bonnet, Timothée, Michael B. Morrissey, and Loeske E. B. Kruuk. "Estimation of Genetic Variance in Fitness, and Inference of Adaptation, When Fitness Follows a Log-Normal Distribution." Journal of Heredity 110, no. 4 (June 2019): 383–95. http://dx.doi.org/10.1093/jhered/esz018.

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AbstractAdditive genetic variance in relative fitness (σA2(w)) is arguably the most important evolutionary parameter in a population because, by Fisher’s fundamental theorem of natural selection (FTNS; Fisher RA. 1930. The genetical theory of natural selection. 1st ed. Oxford: Clarendon Press), it represents the rate of adaptive evolution. However, to date, there are few estimates of σA2(w) in natural populations. Moreover, most of the available estimates rely on Gaussian assumptions inappropriate for fitness data, with unclear consequences. “Generalized linear animal models” (GLAMs) tend to be more appropriate for fitness data, but they estimate parameters on a transformed (“latent”) scale that is not directly interpretable for inferences on the data scale. Here we exploit the latest theoretical developments to clarify how best to estimate quantitative genetic parameters for fitness. Specifically, we use computer simulations to confirm a recently developed analog of the FTNS in the case when expected fitness follows a log-normal distribution. In this situation, the additive genetic variance in absolute fitness on the latent log-scale (σA2(l)) equals (σA2(w)) on the data scale, which is the rate of adaptation within a generation. However, due to inheritance distortion, the change in mean relative fitness between generations exceeds σA2(l) and equals (exp⁡(σA2(l))−1). We illustrate why the heritability of fitness is generally low and is not a good measure of the rate of adaptation. Finally, we explore how well the relevant parameters can be estimated by animal models, comparing Gaussian models with Poisson GLAMs. Our results illustrate 1) the correspondence between quantitative genetics and population dynamics encapsulated in the FTNS and its log-normal-analog and 2) the appropriate interpretation of GLAM parameter estimates.

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Dean, A. M. "A molecular investigation of genotype by environment interactions." Genetics 139, no. 1 (January 1, 1995): 19–33. http://dx.doi.org/10.1093/genetics/139.1.19.

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Abstract The fitnesses conferred by seven lactose operons, which had been transduced into a common genetic background from natural isolates of Escherichia coli, were determined during competition for growth rate-limiting quantities of galactosyl-glycerol, a naturally occurring galactoside. The fitnesses of these same operons have been previously determined on lactose and three artificial galactosides, lactulose, methyl-galactoside and galactosyl-arabinose. Analysis suggests that although marked genotype by environment interactions occur, changes in the fitness rankings are rare. The relative activities of the beta-galactosidases and the permeases were determined on galactosyl-glycerol, lactose, lactulose and methyl-galactoside. Both enzymes display considerable kinetic variation. The beta-galactosidase alleles provide no evidence for genotype by environment interactions at the level of enzyme activity. The permease alleles display genotype by environment interactions with a few causing changes in activity rankings. The contributions to fitness made by the permeases and the beta-galactosidases were partitioned using metabolic control analysis. Most of the genotype by environment interaction at the level of fitness is generated by changes in the distribution of control among steps in the pathway, particularly at the permease where large control coefficients ensure that its kinetic variation has marked fitness effects. Indeed, changes in activity rankings at the permease account for the few changes in fitness rankings. In contrast, the control coefficients of the beta-galactosidase are sufficiently small that its kinetic variation is in, or close to, the neutral limit. The selection coefficients are larger on the artificial galactosides because the control coefficients of the permease and beta-galactosidase are larger. The flux summation theorem requires that control coefficients associated with other steps in the pathway must be reduced, implying that the selection at these steps will be less intense on the artificial galactosides. This suggests that selection intensities need not be greater in novel environments.

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ZHANG, MENGJIE. "IMPROVING OBJECT DETECTION PERFORMANCE WITH GENETIC PROGRAMMING." International Journal on Artificial Intelligence Tools 16, no. 05 (October 2007): 849–73. http://dx.doi.org/10.1142/s0218213007003576.

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This paper describes three developments to improve object detection performance using genetic programming. The first investigates three feature sets, the second investigates a new fitness function, and the third introduces a two phase learning method using genetic programming. This approach is examined on three object detection problems of increasing difficulty and compared with a neural network approach. The two phase GP approach with the new fitness function and the local concentric circular region features achieved the best results. The results suggest that the concentric circular pixel statistics are more effective than the square features for these object detection problems. The fitness function with program size is more effective and more efficient than without for these object detection problems and the evolved genetic programs using this fitness function are much shorter and easier to interpret. The two phase GP approach is more effective and more efficient than the single stage GP approach, and also more effective than the neural network approach on these problems using the same set of features.

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Penke, Lars, Jaap J. A. Denissen, and Geoffrey F. Miller. "The evolutionary genetics of personality." European Journal of Personality 21, no. 5 (August 2007): 549–87. http://dx.doi.org/10.1002/per.629.

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Genetic influences on personality differences are ubiquitous, but their nature is not well understood. A theoretical framework might help, and can be provided by evolutionary genetics. We assess three evolutionary genetic mechanisms that could explain genetic variance in personality differences: selective neutrality, mutation‐selection balance, and balancing selection. Based on evolutionary genetic theory and empirical results from behaviour genetics and personality psychology, we conclude that selective neutrality is largely irrelevant, that mutation‐selection balance seems best at explaining genetic variance in intelligence, and that balancing selection by environmental heterogeneity seems best at explaining genetic variance in personality traits. We propose a general model of heritable personality differences that conceptualises intelligence as fitness components and personality traits as individual reaction norms of genotypes across environments, with different fitness consequences in different environmental niches. We also discuss the place of mental health in the model. This evolutionary genetic framework highlights the role of gene‐environment interactions in the study of personality, yields new insight into the person‐situation‐debate and the structure of personality, and has practical implications for both quantitative and molecular genetic studies of personality. Copyright © 2007 John Wiley & Sons, Ltd.

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Forhad, Md Shafiul Alam, Md Sabir Hossain, Mohammad Obaidur Rahman, Md Mostafizur Rahaman, Md Mokammel Haque, and Muhammad Kamrul Hossain Patwary. "An improved fitness function for automated cryptanalysis using genetic algorithm." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 2 (February 1, 2019): 643. http://dx.doi.org/10.11591/ijeecs.v13.i2.pp643-648.

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Genetic Algorithm (GA) is a popular desire for the researchers for creating an automated cryptanalysis system. GA strategy is useful for many problems. Genetic Algorithms try to solve problems by using genetic processes. Different techniques for deciding on fitness function relying on the ciphers have proposed by different researchers. The most necessary component is to set such a fitness function that can evaluate different types of ciphers on the identical scale. In this paper, we have proposed a combined fitness function that is valid for great sorts of ciphers. We use GA to select the fitness function. We have bought the higher result after imposing our proposed method.

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Journal articles on the topic 'Genetic Fitness'

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