407 research outputs found
Measuring the Evolvability Landscape to study Neutrality
This theoretical work defines the measure of autocorrelation of evolvability
in the context of neutral fitness landscape. This measure has been studied on
the classical MAX-SAT problem. This work highlight a new characteristic of
neutral fitness landscapes which allows to design new adapted metaheuristic
Degeneracy: a design principle for achieving robustness and evolvability
Robustness, the insensitivity of some of a biological system's
functionalities to a set of distinct conditions, is intimately linked to
fitness. Recent studies suggest that it may also play a vital role in enabling
the evolution of species. Increasing robustness, so is proposed, can lead to
the emergence of evolvability if evolution proceeds over a neutral network that
extends far throughout the fitness landscape. Here, we show that the design
principles used to achieve robustness dramatically influence whether robustness
leads to evolvability. In simulation experiments, we find that purely redundant
systems have remarkably low evolvability while degenerate, i.e. partially
redundant, systems tend to be orders of magnitude more evolvable. Surprisingly,
the magnitude of observed variation in evolvability can neither be explained by
differences in the size nor the topology of the neutral networks. This suggests
that degeneracy, a ubiquitous characteristic in biological systems, may be an
important enabler of natural evolution. More generally, our study provides
valuable new clues about the origin of innovations in complex adaptive systems.Comment: Accepted in the Journal of Theoretical Biology (Nov 2009
On the Neutrality of Flowshop Scheduling Fitness Landscapes
Solving efficiently complex problems using metaheuristics, and in particular
local searches, requires incorporating knowledge about the problem to solve. In
this paper, the permutation flowshop problem is studied. It is well known that
in such problems, several solutions may have the same fitness value. As this
neutrality property is an important one, it should be taken into account during
the design of optimization methods. Then in the context of the permutation
flowshop, a deep landscape analysis focused on the neutrality property is
driven and propositions on the way to use this neutrality to guide efficiently
the search are given.Comment: Learning and Intelligent OptimizatioN Conference (LION 5), Rome :
Italy (2011
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
Critical properties of complex fitness landscapes
Evolutionary adaptation is the process that increases the fit of a population
to the fitness landscape it inhabits. As a consequence, evolutionary dynamics
is shaped, constrained, and channeled, by that fitness landscape. Much work has
been expended to understand the evolutionary dynamics of adapting populations,
but much less is known about the structure of the landscapes. Here, we study
the global and local structure of complex fitness landscapes of interacting
loci that describe protein folds or sets of interacting genes forming pathways
or modules. We find that in these landscapes, high peaks are more likely to be
found near other high peaks, corroborating Kauffman's "Massif Central"
hypothesis. We study the clusters of peaks as a function of the ruggedness of
the landscape and find that this clustering allows peaks to form interconnected
networks. These networks undergo a percolation phase transition as a function
of minimum peak height, which indicates that evolutionary trajectories that
take no more than two mutations to shift from peak to peak can span the entire
genetic space. These networks have implications for evolution in rugged
landscapes, allowing adaptation to proceed after a local fitness peak has been
ascended.Comment: 7 pages, 6 figures, requires alifex11.sty. To appear in Proceedings
of 12th International Conference on Artificial Lif
Degenerate neutrality creates evolvable fitness landscapes
Understanding how systems can be designed to be evolvable is fundamental to
research in optimization, evolution, and complex systems science. Many
researchers have thus recognized the importance of evolvability, i.e. the
ability to find new variants of higher fitness, in the fields of biological
evolution and evolutionary computation. Recent studies by Ciliberti et al
(Proc. Nat. Acad. Sci., 2007) and Wagner (Proc. R. Soc. B., 2008) propose a
potentially important link between the robustness and the evolvability of a
system. In particular, it has been suggested that robustness may actually lead
to the emergence of evolvability. Here we study two design principles,
redundancy and degeneracy, for achieving robustness and we show that they have
a dramatically different impact on the evolvability of the system. In
particular, purely redundant systems are found to have very little evolvability
while systems with degeneracy, i.e. distributed robustness, can be orders of
magnitude more evolvable. These results offer insights into the general
principles for achieving evolvability and may prove to be an important step
forward in the pursuit of evolvable representations in evolutionary
computation
Strong Selection Significantly Increases Epistatic Interactions in the Long-Term Evolution of a Protein
Epistatic interactions between residues determine a protein's adaptability
and shape its evolutionary trajectory. When a protein experiences a changed
environment, it is under strong selection to find a peak in the new fitness
landscape. It has been shown that strong selection increases epistatic
interactions as well as the ruggedness of the fitness landscape, but little is
known about how the epistatic interactions change under selection in the
long-term evolution of a protein. Here we analyze the evolution of epistasis in
the protease of the human immunodeficiency virus type 1 (HIV-1) using protease
sequences collected for almost a decade from both treated and untreated
patients, to understand how epistasis changes and how those changes impact the
long-term evolvability of a protein. We use an information-theoretic proxy for
epistasis that quantifies the co-variation between sites, and show that
positive information is a necessary (but not sufficient) condition that detects
epistasis in most cases. We analyze the "fossils" of the evolutionary
trajectories of the protein contained in the sequence data, and show that
epistasis continues to enrich under strong selection, but not for proteins
whose environment is unchanged. The increase in epistasis compensates for the
information loss due to sequence variability brought about by treatment, and
facilitates adaptation in the increasingly rugged fitness landscape of
treatment. While epistasis is thought to enhance evolvability via
valley-crossing early-on in adaptation, it can hinder adaptation later when the
landscape has turned rugged. However, we find no evidence that the HIV-1
protease has reached its potential for evolution after 9 years of adapting to a
drug environment that itself is constantly changing.Comment: 25 pages, 9 figures, plus Supplementary Material including
Supplementary Text S1-S7, Supplementary Tables S1-S2, and Supplementary
Figures S1-2. Version that appears in PLoS Genetic
Flexible couplings: diffusing neuromodulators and adaptive robotics
Recent years have seen the discovery of freely diffusing gaseous neurotransmitters, such as nitric oxide (NO), in biological nervous systems. A type of artificial neural network (ANN) inspired by such gaseous signaling, the GasNet, has previously been shown to be more evolvable than traditional ANNs when used as an artificial nervous system in an evolutionary robotics setting, where evolvability means consistent speed to very good solutions¿here, appropriate sensorimotor behavior-generating systems. We present two new versions of the GasNet, which take further inspiration from the properties of neuronal gaseous signaling. The plexus model is inspired by the extraordinary NO-producing cortical plexus structure of neural fibers and the properties of the diffusing NO signal it generates. The receptor model is inspired by the mediating action of neurotransmitter receptors. Both models are shown to significantly further improve evolvability. We describe a series of analyses suggesting that the reasons for the increase in evolvability are related to the flexible loose coupling of distinct signaling mechanisms, one ¿chemical¿ and one ¿electrical.
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