2,289 research outputs found
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Diversity is an important factor in evolutionary algorithms to prevent
premature convergence towards a single local optimum. In order to maintain
diversity throughout the process of evolution, various means exist in
literature. We analyze approaches to diversity that (a) have an explicit and
quantifiable influence on fitness at the individual level and (b) require no
(or very little) additional domain knowledge such as domain-specific distance
functions. We also introduce the concept of genealogical diversity in a broader
study. We show that employing these approaches can help evolutionary algorithms
for global optimization in many cases.Comment: GECCO '18: Genetic and Evolutionary Computation Conference, 2018,
Kyoto, Japa
Bacterial microevolution and the Pangenome
The comparison of multiple genome sequences sampled from a bacterial population reveals considerable diversity in both the core and the accessory parts of the pangenome. This diversity can be analysed in terms of microevolutionary events that took place since the genomes shared a common ancestor, especially deletion, duplication, and recombination. We review the basic modelling ingredients used implicitly or explicitly when performing such a pangenome analysis. In particular, we describe a basic neutral phylogenetic framework of bacterial pangenome microevolution, which is not incompatible with evaluating the role of natural selection. We survey the different ways in which pangenome data is summarised in order to be included in microevolutionary models, as well as the main methodological approaches that have been proposed to reconstruct pangenome microevolutionary history
Self-adaptive fitness in evolutionary processes
Most optimization algorithms or methods in artificial intelligence can be regarded as evolutionary processes. They start from (basically) random guesses and produce increasingly better results with respect to a given target function, which is defined by the process's designer. The value of the achieved results is communicated to the evolutionary process via a fitness function that is usually somewhat correlated with the target function but does not need to be exactly the same. When the values of the fitness function change purely for reasons intrinsic to the evolutionary process, i.e., even though the externally motivated goals (as represented by the target function) remain constant, we call that phenomenon self-adaptive fitness. We trace the phenomenon of self-adaptive fitness back to emergent goals in artificial chemistry systems, for which we develop a new variant based on neural networks. We perform an in-depth analysis of diversity-aware evolutionary algorithms as a prime example of how to effectively integrate self-adaptive fitness into evolutionary processes. We sketch the concept of productive fitness as a new tool to reason about the intrinsic goals of evolution. We introduce the pattern of scenario co-evolution, which we apply to a reinforcement learning agent competing against an evolutionary algorithm to improve performance and generate hard test cases and which we also consider as a more general pattern for software engineering based on a solid formal framework. Multiple connections to related topics in natural computing, quantum computing and artificial intelligence are discovered and may shape future research in the combined fields.Die meisten Optimierungsalgorithmen und die meisten Verfahren in Bereich kĂŒnstlicher Intelligenz können als evolutionĂ€re Prozesse aufgefasst werden. Diese beginnen mit (prinzipiell) zufĂ€llig geratenen Lösungskandidaten und erzeugen dann immer weiter verbesserte Ergebnisse fĂŒr gegebene Zielfunktion, die der Designer des gesamten Prozesses definiert hat. Der Wert der erreichten Ergebnisse wird dem evolutionĂ€ren Prozess durch eine Fitnessfunktion mitgeteilt, die normalerweise in gewissem Rahmen mit der Zielfunktion korreliert ist, aber auch nicht notwendigerweise mit dieser identisch sein muss. Wenn die Werte der Fitnessfunktion sich allein aus fĂŒr den evolutionĂ€ren Prozess intrinsischen GrĂŒnden Ă€ndern, d.h. auch dann, wenn die extern motivierten Ziele (reprĂ€sentiert durch die Zielfunktion) konstant bleiben, nennen wir dieses PhĂ€nomen selbst-adaptive Fitness. Wir verfolgen das PhĂ€nomen der selbst-adaptiven Fitness zurĂŒck bis zu kĂŒnstlichen Chemiesystemen (artificial chemistry systems), fĂŒr die wir eine neue Variante auf Basis neuronaler Netze entwickeln. Wir fĂŒhren eine tiefgreifende Analyse diversitĂ€tsbewusster evolutionĂ€rer Algorithmen durch, welche wir als Paradebeispiel fĂŒr die effektive Integration von selbst-adaptiver Fitness in evolutionĂ€re Prozesse betrachten. Wir skizzieren das Konzept der produktiven Fitness als ein neues Werkzeug zur Untersuchung von intrinsischen Zielen der Evolution. Wir fĂŒhren das Muster der Szenarien-Ko-Evolution (scenario co-evolution) ein und wenden es auf einen Agenten an, der mittels verstĂ€rkendem Lernen (reinforcement learning) mit einem evolutionĂ€ren Algorithmus darum wetteifert, seine Leistung zu erhöhen bzw. hĂ€rtere Testszenarien zu finden. Wir erkennen dieses Muster auch in einem generelleren Kontext als formale Methode in der Softwareentwicklung. Wir entdecken mehrere Verbindungen der besprochenen PhĂ€nomene zu Forschungsgebieten wie natural computing, quantum computing oder kĂŒnstlicher Intelligenz, welche die zukĂŒnftige Forschung in den kombinierten Forschungsgebieten prĂ€gen könnten
Practice-oriented controversies and borrowed epistemic credibility in current evolutionary biology: phylogeography as a case study
Although there is increasing recognition that theory and practice in science are intimately intertwined, philosophy of science perspectives on scientific controversies have been historically focused on theory rather than practice. As a step in the construction of frameworks for understanding controversies linked to scientific practices, here we introduce the notion of borrowed epistemic credibility (BEC), to describe the situation in which scientists, in order to garner support for their own stances, exploit similarities between tenets in their own field and accepted statements or positions properly developed within other areas of expertise. We illustrate the scope of application of our proposal with the analysis of a heavily methods-grounded, recent controversy in phylogeography, a biological subdiscipline concerned with the study of the historical causes of biogeographical variation through population genetics- and phylogenetics-based computer analyses of diversity in DNA sequences, both within species and between closely related taxa. Toward this end, we briefly summarize the arguments proposed by selected authors representing each side of the controversy: the ânested clade analysisâ school versus the âstatistical phylogeographyâ orientation. We claim that whereas both phylogeographic âresearch stylesâ borrow epistemic credibility from sources such as formal logic, the familiarity of results from other scientific areas, the authority of prominent scientists, or the presumed superiority of quantitative vs. verbal reasoning, âtheoryâ plays essentially no role as a foundation of the controversy. Besides underscoring the importance of strictly methodological and other non-theoretical aspects of controversies in current evolutionary biology, our analysis suggests a perspective with potential usefulness for the re-examination of more general philosophy of biology issues, such as the nature of historical inference, rationality, justification, and objectivity
Non-linear regression models for Approximate Bayesian Computation
Approximate Bayesian inference on the basis of summary statistics is
well-suited to complex problems for which the likelihood is either
mathematically or computationally intractable. However the methods that use
rejection suffer from the curse of dimensionality when the number of summary
statistics is increased. Here we propose a machine-learning approach to the
estimation of the posterior density by introducing two innovations. The new
method fits a nonlinear conditional heteroscedastic regression of the parameter
on the summary statistics, and then adaptively improves estimation using
importance sampling. The new algorithm is compared to the state-of-the-art
approximate Bayesian methods, and achieves considerable reduction of the
computational burden in two examples of inference in statistical genetics and
in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and
Computin
Computational phylogenetics and the classification of South American languages
In recent years, South Americanist linguists have embraced computational phylogenetic methods to resolve the numerous outstanding questions about the genealogi- cal relationships among the languages of the continent. We provide a critical review of the methods and language classification results that have accumulated thus far, emphasizing the superiority of character-based methods over distance-based ones and the importance of develop- ing adequate comparative datasets for producing well- resolved classifications
Chloroplast microsatellites: measures of genetic diversity and the effect of homoplasy
Chloroplast microsatellites have been widely used in population genetic
studies of conifers in recent years. However, their haplotype configurations
suggest that they could have high levels of homoplasy, thus limiting the power
of these molecular markers. A coalescent-based computer simulation was used to
explore the influence of homoplasy on measures of genetic diversity based on
chloroplast microsatellites. The conditions of the simulation were defined to
fit isolated populations originating from the colonization of one single
haplotype into an area left available after a glacial retreat. Simulated data
were compared with empirical data available from the literature for a species
of Pinus that has expanded north after the Last Glacial Maximum. In the
evaluation of genetic diversity, homoplasy was found to have little influence
on Nei's unbiased haplotype diversity (H(E)) while Goldstein's genetic distance
estimates (D2sh) were much more affected. The effect of the number of
chloroplast microsatellite loci for evaluation of genetic diversity is also
discussed
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