2,282 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
A Complete Characterization of Statistical Query Learning with Applications to Evolvability
Statistical query (SQ) learning model of Kearns (1993) is a natural
restriction of the PAC learning model in which a learning algorithm is allowed
to obtain estimates of statistical properties of the examples but cannot see
the examples themselves. We describe a new and simple characterization of the
query complexity of learning in the SQ learning model. Unlike the previously
known bounds on SQ learning our characterization preserves the accuracy and the
efficiency of learning. The preservation of accuracy implies that that our
characterization gives the first characterization of SQ learning in the
agnostic learning framework. The preservation of efficiency is achieved using a
new boosting technique and allows us to derive a new approach to the design of
evolutionary algorithms in Valiant's (2006) model of evolvability. We use this
approach to demonstrate the existence of a large class of monotone evolutionary
learning algorithms based on square loss performance estimation. These results
differ significantly from the few known evolutionary algorithms and give
evidence that evolvability in Valiant's model is a more versatile phenomenon
than there had been previous reason to suspect.Comment: Simplified Lemma 3.8 and it's application
The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks
We investigate how scale-free (SF) and Erdos-Renyi (ER) topologies affect the
interplay between evolvability and robustness of model gene regulatory networks
with Boolean threshold dynamics. In agreement with Oikonomou and Cluzel (2006)
we find that networks with SFin topologies, that is SF topology for incoming
nodes and ER topology for outgoing nodes, are significantly more evolvable
towards specific oscillatory targets than networks with ER topology for both
incoming and outgoing nodes. Similar results are found for networks with SFboth
and SFout topologies. The functionality of the SFout topology, which most
closely resembles the structure of biological gene networks (Babu et al.,
2004), is compared to the ER topology in further detail through an extension to
multiple target outputs, with either an oscillatory or a non-oscillatory
nature. For multiple oscillatory targets of the same length, the differences
between SFout and ER networks are enhanced, but for non-oscillatory targets
both types of networks show fairly similar evolvability. We find that SF
networks generate oscillations much more easily than ER networks do, and this
may explain why SF networks are more evolvable than ER networks are for
oscillatory phenotypes. In spite of their greater evolvability, we find that
networks with SFout topologies are also more robust to mutations than ER
networks. Furthermore, the SFout topologies are more robust to changes in
initial conditions (environmental robustness). For both topologies, we find
that once a population of networks has reached the target state, further
neutral evolution can lead to an increase in both the mutational robustness and
the environmental robustness to changes in initial conditions.Comment: 16 pages, 15 figure
Evolution with Drifting Targets
We consider the question of the stability of evolutionary algorithms to
gradual changes, or drift, in the target concept. We define an algorithm to be
resistant to drift if, for some inverse polynomial drift rate in the target
function, it converges to accuracy 1 -- \epsilon , with polynomial resources,
and then stays within that accuracy indefinitely, except with probability
\epsilon , at any one time. We show that every evolution algorithm, in the
sense of Valiant (2007; 2009), can be converted using the Correlational Query
technique of Feldman (2008), into such a drift resistant algorithm. For certain
evolutionary algorithms, such as for Boolean conjunctions, we give bounds on
the rates of drift that they can resist. We develop some new evolution
algorithms that are resistant to significant drift. In particular, we give an
algorithm for evolving linear separators over the spherically symmetric
distribution that is resistant to a drift rate of O(\epsilon /n), and another
algorithm over the more general product normal distributions that resists a
smaller drift rate.
The above translation result can be also interpreted as one on the robustness
of the notion of evolvability itself under changes of definition. As a second
result in that direction we show that every evolution algorithm can be
converted to a quasi-monotonic one that can evolve from any starting point
without the performance ever dipping significantly below that of the starting
point. This permits the somewhat unnatural feature of arbitrary performance
degradations to be removed from several known robustness translations
Fitness landscape of the cellular automata majority problem: View from the Olympus
In this paper we study cellular automata (CAs) that perform the computational
Majority task. This task is a good example of what the phenomenon of emergence
in complex systems is. We take an interest in the reasons that make this
particular fitness landscape a difficult one. The first goal is to study the
landscape as such, and thus it is ideally independent from the actual
heuristics used to search the space. However, a second goal is to understand
the features a good search technique for this particular problem space should
possess. We statistically quantify in various ways the degree of difficulty of
searching this landscape. Due to neutrality, investigations based on sampling
techniques on the whole landscape are difficult to conduct. So, we go exploring
the landscape from the top. Although it has been proved that no CA can perform
the task perfectly, several efficient CAs for this task have been found.
Exploiting similarities between these CAs and symmetries in the landscape, we
define the Olympus landscape which is regarded as the ''heavenly home'' of the
best local optima known (blok). Then we measure several properties of this
subspace. Although it is easier to find relevant CAs in this subspace than in
the overall landscape, there are structural reasons that prevent a searcher
from finding overfitted CAs in the Olympus. Finally, we study dynamics and
performance of genetic algorithms on the Olympus in order to confirm our
analysis and to find efficient CAs for the Majority problem with low
computational cost
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
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