1,006 research outputs found
Distribution-Independent Evolvability of Linear Threshold Functions
Valiant's (2007) model of evolvability models the evolutionary process of
acquiring useful functionality as a restricted form of learning from random
examples. Linear threshold functions and their various subclasses, such as
conjunctions and decision lists, play a fundamental role in learning theory and
hence their evolvability has been the primary focus of research on Valiant's
framework (2007). One of the main open problems regarding the model is whether
conjunctions are evolvable distribution-independently (Feldman and Valiant,
2008). We show that the answer is negative. Our proof is based on a new
combinatorial parameter of a concept class that lower-bounds the complexity of
learning from correlations.
We contrast the lower bound with a proof that linear threshold functions
having a non-negligible margin on the data points are evolvable
distribution-independently via a simple mutation algorithm. Our algorithm
relies on a non-linear loss function being used to select the hypotheses
instead of 0-1 loss in Valiant's (2007) original definition. The proof of
evolvability requires that the loss function satisfies several mild conditions
that are, for example, satisfied by the quadratic loss function studied in
several other works (Michael, 2007; Feldman, 2009; Valiant, 2010). An important
property of our evolution algorithm is monotonicity, that is the algorithm
guarantees evolvability without any decreases in performance. Previously,
monotone evolvability was only shown for conjunctions with quadratic loss
(Feldman, 2009) or when the distribution on the domain is severely restricted
(Michael, 2007; Feldman, 2009; Kanade et al., 2010
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Natural evolution has produced a tremendous diversity of functional
organisms. Many believe an essential component of this process was the
evolution of evolvability, whereby evolution speeds up its ability to innovate
by generating a more adaptive pool of offspring. One hypothesized mechanism for
evolvability is developmental canalization, wherein certain dimensions of
variation become more likely to be traversed and others are prevented from
being explored (e.g. offspring tend to have similarly sized legs, and mutations
affect the length of both legs, not each leg individually). While ubiquitous in
nature, canalization almost never evolves in computational simulations of
evolution. Not only does that deprive us of in silico models in which to study
the evolution of evolvability, but it also raises the question of which
conditions give rise to this form of evolvability. Answering this question
would shed light on why such evolvability emerged naturally and could
accelerate engineering efforts to harness evolution to solve important
engineering challenges. In this paper we reveal a unique system in which
canalization did emerge in computational evolution. We document that genomes
entrench certain dimensions of variation that were frequently explored during
their evolutionary history. The genetic representation of these organisms also
evolved to be highly modular and hierarchical, and we show that these
organizational properties correlate with increased fitness. Interestingly, the
type of computational evolutionary experiment that produced this evolvability
was very different from traditional digital evolution in that there was no
objective, suggesting that open-ended, divergent evolutionary processes may be
necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi
Evolvability of Chaperonin Substrate Proteins
Molecular chaperones ensure that their substrate proteins reach the functional native state, and prevent their aggregation. Recently, an additional function was proposed for molecular chaperones: they serve as buffers (_capacitors_) for evolution by permitting their substrate proteins to mutate and at the same time still allowing them to fold productively.

Using pairwise alignments of _E. coli_ genes with genes from other gamma-proteobacteria, we showed that the described buffering effect cannot be observed among substrate proteins of GroEL, an essential chaperone in _E. coli_. Instead, we find that GroEL substrate proteins evolve less than other soluble _E. coli_ proteins. We analyzed several specific structural and biophysical properties of proteins to assess their influence on protein evolution and to find out why specifically GroEL substrates do not show the expected higher divergence from their orthologs.

Our results culminate in four main findings: *1.* We find little evidence that GroEL in _E. coli_ acts as a capacitor for evolution _in vivo_. *2.* GroEL substrates evolved less than other _E. coli_ proteins. *3.* Predominantly structural features appear to be a strong determinant of evolutionary rate. *4.* Besides size, hydrophobicity is a criterion for exclusion for a protein as a chaperonin substrate
Robust Learning under Strong Noise via {SQs}
This work provides several new insights on the robustness of Kearns' statistical query framework against challenging label-noise models. First, we build on a recent result by \cite{DBLP:journals/corr/abs-2006-04787} that showed noise tolerance of distribution-independently evolvable concept classes under Massart noise. Specifically, we extend their characterization to more general noise models, including the Tsybakov model which considerably generalizes the Massart condition by allowing the flipping probability to be arbitrarily close to for a subset of the domain. As a corollary, we employ an evolutionary algorithm by \cite{DBLP:conf/colt/KanadeVV10} to obtain the first polynomial time algorithm with arbitrarily small excess error for learning linear threshold functions over any spherically symmetric distribution in the presence of spherically symmetric Tsybakov noise. Moreover, we posit access to a stronger oracle, in which for every labeled example we additionally obtain its flipping probability. In this model, we show that every SQ learnable class admits an efficient learning algorithm with OPT + misclassification error for a broad class of noise models. This setting substantially generalizes the widely-studied problem of classification under RCN with known noise rate, and corresponds to a non-convex optimization problem even when the noise function -- i.e. the flipping probabilities of all points -- is known in advance
Evolvability-guided Optimization of Linear Deformation Setups for Evolutionary Design Optimization
Richter A. Evolvability-guided Optimization of Linear Deformation Setups for Evolutionary Design Optimization. Bielefeld: Universität Bielefeld; 2019.Andreas Richter gratefully acknowledges the financial support from Honda Research Institute Europe (HRI-EU).This thesis targets efficient solutions for optimal representation setups for evolutionary design optimization problems. The representation maps the abstract parameters of an optimizer to a meaningful variation of the design model, e.g., the shape of a car. Thereby, it determines the convergence speed to and the quality of the final result. Thus, engineers are eager to employ well-tuned representations to achieve high-quality design solutions. But, setting up optimal representations is a cumbersome process because the setup procedure requires detailed knowledge about the objective functions, e.g., a fluid dynamics simulation, and the parameters of the employed representation itself. Thus, we target efficient routines to set up representations automatically to support engineers from their tedious, partly manual work.
Inspired by the concept of evolvability, we present novel quality criteria for the evaluation of linear deformations commonly applied as representations. We define and analyze the criteria variability, regularity, and improvement potential which measure the expected quality and convergence speed of an evolutionary design optimization process based on the linear deformation setup. Moreover, we target the efficient optimization of deformation setups with respect to these three criteria. In dynamic design optimization scenarios a suitable compromise between exploration and exploitation is crucial for efficient solutions. We discuss the construction of optimal compromises for these dynamic scenarios with our criteria because they characterize exploration and exploitation.
As a result an engineer can initialize and adjust the deformation setup for improved convergence speed of a design process and for enhanced quality of the design solutions with our methods
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