18,472 research outputs found
Integrated information increases with fitness in the evolution of animats
One of the hallmarks of biological organisms is their ability to integrate
disparate information sources to optimize their behavior in complex
environments. How this capability can be quantified and related to the
functional complexity of an organism remains a challenging problem, in
particular since organismal functional complexity is not well-defined. We
present here several candidate measures that quantify information and
integration, and study their dependence on fitness as an artificial agent
("animat") evolves over thousands of generations to solve a navigation task in
a simple, simulated environment. We compare the ability of these measures to
predict high fitness with more conventional information-theoretic processing
measures. As the animat adapts by increasing its "fit" to the world,
information integration and processing increase commensurately along the
evolutionary line of descent. We suggest that the correlation of fitness with
information integration and with processing measures implies that high fitness
requires both information processing as well as integration, but that
information integration may be a better measure when the task requires memory.
A correlation of measures of information integration (but also information
processing) and fitness strongly suggests that these measures reflect the
functional complexity of the animat, and that such measures can be used to
quantify functional complexity even in the absence of fitness data.Comment: 27 pages, 8 figures, one supplementary figure. Three supplementary
video files available on request. Version commensurate with published text in
PLoS Comput. Bio
The use of information theory in evolutionary biology
Information is a key concept in evolutionary biology. Information is stored
in biological organism's genomes, and used to generate the organism as well as
to maintain and control it. Information is also "that which evolves". When a
population adapts to a local environment, information about this environment is
fixed in a representative genome. However, when an environment changes,
information can be lost. At the same time, information is processed by animal
brains to survive in complex environments, and the capacity for information
processing also evolves. Here I review applications of information theory to
the evolution of proteins as well as to the evolution of information processing
in simulated agents that adapt to perform a complex task.Comment: 25 pages, 7 figures. To appear in "The Year in Evolutionary Biology",
of the Annals of the NY Academy of Science
Universality and predictability in molecular quantitative genetics
Molecular traits, such as gene expression levels or protein binding
affinities, are increasingly accessible to quantitative measurement by modern
high-throughput techniques. Such traits measure molecular functions and, from
an evolutionary point of view, are important as targets of natural selection.
We review recent developments in evolutionary theory and experiments that are
expected to become building blocks of a quantitative genetics of molecular
traits. We focus on universal evolutionary characteristics: these are largely
independent of a trait's genetic basis, which is often at least partially
unknown. We show that universal measurements can be used to infer selection on
a quantitative trait, which determines its evolutionary mode of conservation or
adaptation. Furthermore, universality is closely linked to predictability of
trait evolution across lineages. We argue that universal trait statistics
extends over a range of cellular scales and opens new avenues of quantitative
evolutionary systems biology
The evolution of representation in simple cognitive networks
Representations are internal models of the environment that can provide
guidance to a behaving agent, even in the absence of sensory information. It is
not clear how representations are developed and whether or not they are
necessary or even essential for intelligent behavior. We argue here that the
ability to represent relevant features of the environment is the expected
consequence of an adaptive process, give a formal definition of representation
based on information theory, and quantify it with a measure R. To measure how R
changes over time, we evolve two types of networks---an artificial neural
network and a network of hidden Markov gates---to solve a categorization task
using a genetic algorithm. We find that the capacity to represent increases
during evolutionary adaptation, and that agents form representations of their
environment during their lifetime. This ability allows the agents to act on
sensorial inputs in the context of their acquired representations and enables
complex and context-dependent behavior. We examine which concepts (features of
the environment) our networks are representing, how the representations are
logically encoded in the networks, and how they form as an agent behaves to
solve a task. We conclude that R should be able to quantify the representations
within any cognitive system, and should be predictive of an agent's long-term
adaptive success.Comment: 36 pages, 10 figures, one Tabl
Evolution of associative learning in chemical networks
Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ’memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells
Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms: support vector regression forecast combinations
The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm
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