760 research outputs found
The Evolutionary Unfolding of Complexity
We analyze the population dynamics of a broad class of fitness functions that
exhibit epochal evolution---a dynamical behavior, commonly observed in both
natural and artificial evolutionary processes, in which long periods of stasis
in an evolving population are punctuated by sudden bursts of change. Our
approach---statistical dynamics---combines methods from both statistical
mechanics and dynamical systems theory in a way that offers an alternative to
current ``landscape'' models of evolutionary optimization. We describe the
population dynamics on the macroscopic level of fitness classes or phenotype
subbasins, while averaging out the genotypic variation that is consistent with
a macroscopic state. Metastability in epochal evolution occurs solely at the
macroscopic level of the fitness distribution. While a balance between
selection and mutation maintains a quasistationary distribution of fitness,
individuals diffuse randomly through selectively neutral subbasins in genotype
space. Sudden innovations occur when, through this diffusion, a genotypic
portal is discovered that connects to a new subbasin of higher fitness
genotypes. In this way, we identify innovations with the unfolding and
stabilization of a new dimension in the macroscopic state space. The
architectural view of subbasins and portals in genotype space clarifies how
frozen accidents and the resulting phenotypic constraints guide the evolution
to higher complexity.Comment: 28 pages, 5 figure
Cell division and migration in a 'genotype' for neural networks
Much research has been dedicated recently to applying genetic algorithms to populations of
neural networks. However, while in real organisms the inherited genotype maps in complex
ways into the resulting phenotype, in most of this research the development process that
creates the individual phenotype is ignored. In this paper we present a model of neural
development which includes cell division and cell migration in addition to axonal growth and
branching. This reflects, in a very simplified way, what happens in the ontogeny of real
organisms. The development process of our artificial organisms shows successive phases of
functional differentiation and specialization. In addition, we find that mutations that affect
different phases of development have very different evolutionary consequences. A single
change in the early stages of cell division/migration can have huge effects on the phenotype
while changes in later stages have usually a less drammatic impact. Sometimes changes that
affect the first developental stages may be retained producing sudden changes in evolutionary
history
Effects of neutral selection on the evolution of molecular species
We introduce a new model of evolution on a fitness landscape possessing a
tunable degree of neutrality. The model allows us to study the general
properties of molecular species undergoing neutral evolution. We find that a
number of phenomena seen in RNA sequence-structure maps are present also in our
general model. Examples are the occurrence of "common" structures which occupy
a fraction of the genotype space which tends to unity as the length of the
genotype increases, and the formation of percolating neutral networks which
cover the genotype space in such a way that a member of such a network can be
found within a small radius of any point in the space. We also describe a
number of new phenomena which appear to be general properties of neutrally
evolving systems. In particular, we show that the maximum fitness attained
during the adaptive walk of a population evolving on such a fitness landscape
increases with increasing degree of neutrality, and is directly related to the
fitness of the most fit percolating network.Comment: 16 pages including 4 postscript figures, typeset in LaTeX2e using the
Elsevier macro package elsart.cl
Centric selection: a way to tune the exploration/exploitation trade-off
In this paper, we study the exploration / exploitation trade-off in cellular
genetic algorithms. We define a new selection scheme, the centric selection,
which is tunable and allows controlling the selective pressure with a single
parameter. The equilibrium model is used to study the influence of the centric
selection on the selective pressure and a new model which takes into account
problem dependent statistics and selective pressure in order to deal with the
exploration / exploitation trade-off is proposed: the punctuated equilibria
model. Performances on the quadratic assignment problem and NK-Landscapes put
in evidence an optimal exploration / exploitation trade-off on both of the
classes of problems. The punctuated equilibria model is used to explain these
results
A multi-agent evolutionary robotics framework to train spiking neural networks
A novel multi-agent evolutionary robotics (ER) based framework, inspired by
competitive evolutionary environments in nature, is demonstrated for training
Spiking Neural Networks (SNN). The weights of a population of SNNs along with
morphological parameters of bots they control in the ER environment are treated
as phenotypes. Rules of the framework select certain bots and their SNNs for
reproduction and others for elimination based on their efficacy in capturing
food in a competitive environment. While the bots and their SNNs are given no
explicit reward to survive or reproduce via any loss function, these drives
emerge implicitly as they evolve to hunt food and survive within these rules.
Their efficiency in capturing food as a function of generations exhibit the
evolutionary signature of punctuated equilibria. Two evolutionary inheritance
algorithms on the phenotypes, Mutation and Crossover with Mutation, are
demonstrated. Performances of these algorithms are compared using ensembles of
100 experiments for each algorithm. We find that Crossover with Mutation
promotes 40% faster learning in the SNN than mere Mutation with a statistically
significant margin.Comment: 9 pages, 11 figure
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