The Genetical Theory of Natural Selection
TL;DR: Although it is true that most text-books of genetics open with a chapter on biometry, closer inspection will reveal that this has little connexion with the body of the work, and that more often than not it is merely belated homage to a once fashionable study.
Abstract: PROBABLY most geneticists to-day are some-what sceptical as to the value of the mathematical treatment of their problems. With the deepest respect, and even awe, for that association of complex symbols and human genius that can bring a universe to heel, they are nevertheless content to let it stand at that, believing that in their own particular line it is, after all, plodding that does it. Although it is true that most text-books of genetics open with a chapter on biometry, closer inspection will reveal that this has little connexion with the body of the work, and that more often than not it is merely belated homage to a once fashionable study. The Genetical Theory of Natural Selection. Dr. R. A. Fisher. Pp. xiv + 272 + 2 plates. (Oxford: Clarendon Press; London: Oxford University Press, 1930.) 17s. 6d. net.
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TL;DR: A model is developed based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game to show how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established.
Abstract: Cooperation in organisms, whether bacteria or primates, has been a difficulty for evolutionary theory since Darwin. On the assumption that interactions between pairs of individuals occur on a probabilistic basis, a model is developed based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game. Deductions from the model, and the results of a computer tournament show how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established. Potential applications include specific aspects of territoriality, mating, and disease.
10,675 citations
Book•
01 Jan 1996TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Abstract: From the Publisher:
"This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book.
-- John H. Holland, Professor, Computer Science and Engineering, and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute.
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.
An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text.
The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
9,933 citations
Cites background from "The Genetical Theory of Natural Sel..."
...Chapter 3: Genetic Algorithms in Scientific Models 75 Fisher (1930) proposed that this process could result in a feedback loop between females' preference for a certain trait and the strength and frequency of that trait in males....
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TL;DR: In this paper, a model is presented to account for the natural selection of what is termed reciprocally altruistic behavior, and the model shows how selection can operate against the cheater (non-reciprocator) in the system.
Abstract: A model is presented to account for the natural selection of what is termed reciprocally altruistic behavior. The model shows how selection can operate against the cheater (non-reciprocator) in the system. Three instances of altruistic behavior are discussed, the evolution of which the model can explain: (1) behavior involved in cleaning symbioses; (2) warning cries in birds; and (3) human reciprocal altruism. Regarding human reciprocal altruism, it is shown that the details of the psychological system that regulates this altruism can be explained by the model. Specifically, friendship, dislike, moralistic aggression, gratitude, sympathy, trust, suspicion, trustworthiness, aspects of guilt, and some forms of dishonesty and hypocrisy can be explained as important adaptations to regulate the altruistic system. Each individual human is seen as possessing altruistic and cheating tendencies, the expression of which is sensitive to developmental variables that were selected to set the tendencies at a balance ap...
9,318 citations
Cites background from "The Genetical Theory of Natural Sel..."
...Cost and benefit were defined above without reference to the ages, and hence reproductive values (Fisher, 1958), of the individuals involved in an altruistic exchange....
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TL;DR: For example, this paper found that females value cues to resource acquisition in potential mates more highly than males, while males valued earning capacity, ambition, industriousness, youth, physical attractiveness, and chastity.
Abstract: Contemporary mate preferences can provide important clues to human reproductive history. Little is known about which characteristics people value in potential mates. Five predictions were made about sex differences in human mate preferences based on evolutionary conceptions of parental investment, sexual selection, human reproductive capacity, and sexual asymmetries regarding certainty of paternity versus maternity. The predictions centered on how each sex valued earning capacity, ambition— industriousness, youth, physical attractiveness, and chastity. Predictions were tested in data from 37 samples drawn from 33 countries located on six continents and five islands (total N = 10,047). For 27 countries, demographic data on actual age at marriage provided a validity check on questionnaire data. Females were found to value cues to resource acquisition in potential mates more highly than males. Characteristics signaling reproductive capacity were valued more by males than by females. These sex differences may reflect different evolutionary selection pressures on human males and females; they provide powerful cross-cultural evidence of current sex differences in reproductive strategies. Discussion focuses on proximate mechanisms underlying mate preferences, consequences for human intrasexual competition, and the limitations of this study.
3,733 citations
Cites background from "The Genetical Theory of Natural Sel..."
...Reproductive value is defined actuarially in units of expected future reproduction - the extent to which persons of a given age and sex will contribute, on average, to the ancestry of future generations (Fisher 1930)....
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...In humans and other mammals, male parental investment tends to be less than female parental investment (Fisher 1930; Trivers 1972; Williams 1975)....
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...Because of the powerful reproductive consequences of preferential mating, it is reasonable to assume that mate preferences will depart from randomness and evolve through sexual selection (Darwin 1859; 1871; Fisher 1930)....
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...However, the fact that each individual must have one parent of each sex, and hence that males and females have equal reproductive value (Fisher 1930), constrains the divergence of male and female life history traits in species with a 1:1 sex ratio....
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TL;DR: This review organizes ideas on the evolution of life histories into more comprehensive theory that makes more readily falsifiable predictions, and examination of different definitions of fitness.
Abstract: This review organizes ideas on the evolution of life histories. The key life-history traits are brood size, size of young, the age distribution of reproductive effort, the interaction of reproductive effort with adutl mortality, and the variation in these traits among an individual's progeny. The general theoretical problem is to predict which combinations of traits will evolve in organisms living in specified circumstances. First consider single traits. Theorists have made the following predictions: (1) Where adult exceeds juvenile mortality, the organism should reproduce only once in its lifetime. Where juvenile exceeds adult mortality, the organism should reproduce several times. (2) Brood size should maximize the number of young surviving to maturity, summed over the lifetime of the parent. But when optimum brood-size varies unpredictably in time, smaller broods should be favored because they decrease the chances of total failure on a given attempt. (3) In expanding populations, selection should minim...
3,422 citations