scholarly journals The Boundedness Illusion: Asymptotic projections from early evolution underestimate evolutionary potential

2018 ◽  
Author(s):  
Michael J Wiser ◽  
Emily L Dolson ◽  
Anya Vostinar ◽  
Richard E Lenski ◽  
Charles Ofria
Keyword(s):  
Early Evolution ◽  
Digital Evolution ◽  
Evolutionary Potential ◽  
Holy Grail ◽  
End Point ◽  
Evolution Experiment ◽  
Best Fit

Open-ended evolution researchers seek to create systems that continually produce new evolutionary outcomes, attempting to reflect the power and diversity of evolution in nature. The specific metrics used (novelty, complexity, diversity, etc) vary by researcher, but the holy grail would be a system where any of these can accumulate indefinitely. Of course, one challenge that we face in reaching this goal is even recognizing if we have succeeded. To determine the evolutionary potential of a system, we must conduct finite experiments; based on their results we can predict how we would expect evolution to progress were it to continue. Here we examine how such predictions might be made and how accurate they might be. We focus on predicting fitness; this metric is often easy to calculate, and correlated with increases in traits like novelty and complexity. For each run in a simple digital evolution experiment, we find the best fit to measured values of fitness, and demonstrate that projecting this fit out usually predicts that fitness will be constrained by an asymptote. Upon extending the experiments, however, we see that fitness often far exceeds this asymptote, belying the boundedness that it implies. Extending past a premature end point allows us to see beyond this "boundedness illusion"

scholarly journals The Boundedness Illusion: Asymptotic projections from early evolution underestimate evolutionary potential

2018 ◽  
Author(s):  
Michael J Wiser ◽  
Emily L Dolson ◽  
Anya Vostinar ◽  
Richard E Lenski ◽  
Charles Ofria
Keyword(s):  
Early Evolution ◽  
Digital Evolution ◽  
Evolutionary Potential ◽  
Holy Grail ◽  
End Point ◽  
Evolution Experiment ◽  
Best Fit

Open-ended evolution researchers seek to create systems that continually produce new evolutionary outcomes, attempting to reflect the power and diversity of evolution in nature. The specific metrics used (novelty, complexity, diversity, etc) vary by researcher, but the holy grail would be a system where any of these can accumulate indefinitely. Of course, one challenge that we face in reaching this goal is even recognizing if we have succeeded. To determine the evolutionary potential of a system, we must conduct finite experiments; based on their results we can predict how we would expect evolution to progress were it to continue. Here we examine how such predictions might be made and how accurate they might be. We focus on predicting fitness; this metric is often easy to calculate, and correlated with increases in traits like novelty and complexity. For each run in a simple digital evolution experiment, we find the best fit to measured values of fitness, and demonstrate that projecting this fit out usually predicts that fitness will be constrained by an asymptote. Upon extending the experiment, however, we see that fitness often far exceeds this asymptote, belying the boundedness that it implies. Extending past a premature end point allows us to see beyond this "boundedness illusion"

scholarly journals The Boundedness Illusion: Asymptotic projections from early evolution underestimate evolutionary potential

2018 ◽  
Author(s):  
Michael J Wiser ◽  
Emily L Dolson ◽  
Anya Vostinar ◽  
Richard E Lenski ◽  
Charles Ofria
Keyword(s):  
Early Evolution ◽  
Digital Evolution ◽  
Evolutionary Potential ◽  
Holy Grail ◽  
End Point ◽  
Evolution Experiment ◽  
Best Fit

Open-ended evolution researchers seek to create systems that continually produce new evolutionary outcomes, attempting to reflect the power and diversity of evolution in nature. The specific metrics used (novelty, complexity, diversity, etc) vary by researcher, but the holy grail would be a system where any of these can accumulate indefinitely. Of course, one challenge that we face in reaching this goal is even recognizing if we have succeeded. To determine the evolutionary potential of a system, we must conduct finite experiments; based on their results we can predict how we would expect evolution to progress were it to continue. Here we examine how such predictions might be made and how accurate they might be. We focus on predicting fitness; this metric is often easy to calculate, and correlated with increases in traits like novelty and complexity. For each run in a simple digital evolution experiment, we find the best fit to measured values of fitness, and demonstrate that projecting this fit out usually predicts that fitness will be constrained by an asymptote. Upon extending the experiment, however, we see that fitness often far exceeds this asymptote, belying the boundedness that it implies. Extending past a premature end point allows us to see beyond this "boundedness illusion"

scholarly journals Spatial resource heterogeneity creates local hotspots of evolutionary potential

2017 ◽  
Author(s):  
Emily Dolson ◽  
Charles Ofria
Keyword(s):  
Explanatory Power ◽  
Biological Evolution ◽  
Biological Data ◽  
Heterogeneous Environments ◽  
Digital Evolution ◽  
Resource Heterogeneity ◽  
Evolutionary Potential ◽  
Local Conditions ◽  
Local Diversity ◽  
Novel Traits

AbstractDo local conditions influence evolution’s ability to produce new traits? Biological data demonstrate that evolutionary processes can be profoundly influenced by local conditions. However, the evolution of novel traits has not been addressed in this context, owing in part to the challenges of performing the necessary experiments with natural organisms. We conduct in silico experiments with the Avida Digital Evolution Platform to address this question. We created eight different spatially heterogeneous environments and ran 100 replicates in each. Within each environment, we examined the distribution of locations where nine different focal traits first evolved. Using spatial statistics methods, we identified regions within each environment that had significantly elevated probabilities of containing the first organism with a given trait (i.e. hotspots of evolutionary potential). Having demonstrated the presence of many such hotspots, we explored three potential mechanisms that could drive the formation of these patterns: proximity of specific resources, variation in local diversity, and variation in the sequence of locations the members of an evolutionary lineage occupy. Resource proximity and local diversity appear to have minimal explanatory power. Lineage paths through space, however, show some promising preliminary trends. If we can understand the processes that create evolutionary hotspots, we will be able to craft environments that are more effective at evolving targeted traits. This capability would be useful both to evolutionary computation, and to efforts to guide biological evolution.

scholarly journals A Drosophila laboratory evolution experiment points to low evolutionary potential under increased temperatures likely to be experienced in the future

2014 ◽  
Vol 27 (9) ◽  
pp. 1859-1868 ◽  
Author(s):  
M. F. Schou ◽  
T. N. Kristensen ◽  
V. Kellermann ◽  
C. Schlötterer ◽  
V. Loeschcke
Keyword(s):  
Evolutionary Potential ◽  
Laboratory Evolution ◽  
The Future ◽  
Evolution Experiment

scholarly journals Comparison of kinetic and end-point diffusion methods for quantitating human serum immunoglobulins

1978 ◽  
Vol 8 (1) ◽  
pp. 23-27
Author(s):  
R N Taylor ◽  
K M Fulford ◽  
A Y Huong
Keyword(s):  
Statistical Analysis ◽  
Human Serum ◽  
Statistical Tests ◽  
Immunoglobulin M ◽  
Frequency Distributions ◽  
End Point ◽  
Serum Immunoglobulins ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Best Fit

Proficiency testing results were used to compare the kinetic and end-point versions of the single radial immunodiffusion method for quantitating human serum immunoglobulins. Statistical analysis of the results indicated that the results were not normally distributed but that the log normal distribution gives the best fit of any of the well-known frequency distributions. Consequently, statistical analysis of immunoglobulin results must be log transformed before parametric statistical tests can be appropriately applied. In general, there were no significant differences in level, precision, or interlaboratory comparability for these two methods. However, levels were different for participants using Hyland reagents, and better interlaboratory comparability was achieved by the end-point assay for immunoglobulin M. There were no significant differences in reported levels when compared by manufacturer within the same method.

Salvation of the diabetic foot: Still a quest for the holy grail?

VASA ◽  
2011 ◽  
Vol 40 (4) ◽  
pp. 267-269 ◽  
Author(s):  
Papanas ◽  
Maltezos ◽  
Edmonds
Keyword(s):  
Diabetic Foot ◽  
Holy Grail

Question Wording Effects in the Assessment of Global Self-Esteem

2000 ◽  
Vol 16 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Martin Dunbar ◽  
Graeme Ford ◽  
Kate Hunt ◽  
Geoff Der
Keyword(s):  
Reading Ability ◽  
Factor Model ◽  
Self Esteem ◽  
Reading Scores ◽  
Poor Reading ◽  
Method Effect ◽  
Two Samples ◽  
Positive Item ◽  
Method Effects ◽  
Best Fit

Summary: Marsh (1996) produced evidence that method effects associated with negatively worded items might be responsible for the results of earlier factor analytic studies that reported finding positive and negative self-esteem factors in the Rosenberg Global self-esteem scale ( Rosenberg, 1965 ). He analyzed data collected from children using a 7-item self-esteem measure. This report details attempts to replicate Marsh 's analysis in data collected from two samples of adults who completed the full 10-item Global Self-Esteem (GSE) scale. The results reported here are similar to those given by Marsh in so much as a correlated uniquenesses model produced a superior fit to the data than the simple one factor model (without correlated uniquenesses) or the often reported two factor (positive and negative self-esteem) model. However, whilst Marsh reported that the best fit was produced by allowing negative item uniquenesses to correlate with each other, the model that produced the best fit to these data was one that contained correlated positive item uniquenesses. Supporting his claim that differential responding to negative and positive self-esteem items reflects a method effect associated with reading ability, Marsh also showed that factors associated with negative and positive items were most distinct among children who had poor reading scores. We report a similar effect among a sample of older adults where the correlation between these factors was compared across two groups who were selected according to their scores on a test of verbal reasoning.

The Quest for the Holy Grail

1981 ◽  
Vol 26 (9) ◽  
pp. 706-707
Author(s):  
Robert S. Siegler
Keyword(s):  
Holy Grail
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