4,305 research outputs found
Neutrality: A Necessity for Self-Adaptation
Self-adaptation is used in all main paradigms of evolutionary computation to
increase efficiency. We claim that the basis of self-adaptation is the use of
neutrality. In the absence of external control neutrality allows a variation of
the search distribution without the risk of fitness loss.Comment: 6 pages, 3 figures, LaTe
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
Properties of selected mutations and genotypic landscapes under Fisher's Geometric Model
The fitness landscape - the mapping between genotypes and fitness -
determines properties of the process of adaptation. Several small genetic
fitness landscapes have recently been built by selecting a handful of
beneficial mutations and measuring fitness of all combinations of these
mutations. Here we generate several testable predictions for the properties of
these landscapes under Fisher's geometric model of adaptation (FGMA). When far
from the fitness optimum, we analytically compute the fitness effect of
beneficial mutations and their epistatic interactions. We show that epistasis
may be negative or positive on average depending on the distance of the
ancestral genotype to the optimum and whether mutations were independently
selected or co-selected in an adaptive walk. Using simulations, we show that
genetic landscapes built from FGMA are very close to an additive landscape when
the ancestral strain is far from the optimum. However, when close to the
optimum, a large diversity of landscape with substantial ruggedness and sign
epistasis emerged. Strikingly, landscapes built from different realizations of
stochastic adaptive walks in the same exact conditions were highly variable,
suggesting that several realizations of small genetic landscapes are needed to
gain information about the underlying architecture of the global adaptive
landscape.Comment: 51 pages, 8 figure
Evolution of Robustness and Plasticity under Environmental Fluctuation: Formulation in terms of Phenotypic Variances
The characterization of plasticity, robustness, and evolvability, an
important issue in biology, is studied in terms of phenotypic fluctuations. By
numerically evolving gene regulatory networks, the proportionality between the
phenotypic variances of epigenetic and genetic origins is confirmed. The former
is given by the variance of the phenotypic fluctuation due to noise in the
developmental process; and the latter, by the variance of the phenotypic
fluctuation due to genetic mutation. The relationship suggests a link between
robustness to noise and to mutation, since robustness can be defined by the
sharpness of the distribution of the phenotype. Next, the proportionality
between the variances is demonstrated to also hold over expressions of
different genes (phenotypic traits) when the system acquires robustness through
the evolution. Then, evolution under environmental variation is numerically
investigated and it is found that both the adaptability to a novel environment
and the robustness are made compatible when a certain degree of phenotypic
fluctuations exists due to noise. The highest adaptability is achieved at a
certain noise level at which the gene expression dynamics are near the critical
state to lose the robustness. Based on our results, we revisit Waddington's
canalization and genetic assimilation with regard to the two types of
phenotypic fluctuations.Comment: 23 pages 11 figure
Dismantling Lamarckism: why descriptions of socio-economic evolution as Lamarckian are misleading
“The original publication is available at www.springerlink.com”. Copyright Springer.This paper addresses the widespread tendency to describe socio-economic evolution as Lamarckian. The difference between Lamarckian and Darwinian replication is clarified. It is shown that a phenotype-genotype distinction must first be established before we can identify Lamarckian transmission. To qualify as Lamarckian inheritance, acquired properties at the phenotypic level must be encoded in a genotype that is passed on to the next generation. Some possible social replicators (or genotypes) are identified, with a view to exploring possible distinctions between genotype and phenotype at the social level. It is concluded that the Lamarckian label does not readily transfer to socio-economic evolution, despite the fact that social genotypes (such as routines) may adapt within any given phenotype (such as an organisation). By contrast, no such problems exist with the description of socio-economic evolution as Darwinian.Peer reviewe
Misinformation, Misrepresentation, and Misuse of Human Behavioral Genetics Research
Kaplan discusses the limitations of human behavioral genetics studies, highlighting the research limitations inherent in studying humans and the narrow policy and legal applicability of results arising from behavioral genetics studies
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
Integrating evolution into ecological modelling: accommodating phenotypic changes in agent based models.
PMCID: PMC3733718This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Evolutionary change is a characteristic of living organisms and forms one of the ways in which species adapt to changed conditions. However, most ecological models do not incorporate this ubiquitous phenomenon. We have developed a model that takes a 'phenotypic gambit' approach and focuses on changes in the frequency of phenotypes (which differ in timing of breeding and fecundity) within a population, using, as an example, seasonal breeding. Fitness per phenotype calculated as the individual's contribution to population growth on an annual basis coincide with the population dynamics per phenotype. Simplified model variants were explored to examine whether the complexity included in the model is justified. Outputs from the spatially implicit model underestimated the number of individuals across all phenotypes. When no phenotype transitions are included (i.e. offspring always inherit their parent's phenotype) numbers of all individuals are always underestimated. We conclude that by using a phenotypic gambit approach evolutionary dynamics can be incorporated into individual based models, and that all that is required is an understanding of the probability of offspring inheriting the parental phenotype
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