397 research outputs found
Evolving Solutions for Design and Management Tasks on Computers
Uninitiated may find it strange that artificial evolution resides among a class of problem solving methods belonging to a field named computational intelligence. Some people still believe that nature's trial-and-error way to adapt subsystems to their environment is a prodigal game at dice that has led to admirable results only due to vast resources of time and space. A rather simple gedankenexperiment, however, reveals that all 10E80 most elementary particles in the universe together with 10E60 tiniest time steps since the begin of time cannot explain the development of even simplest bacterial genomes by pure random sampling. Organic evolution must have found a more efficient way to develop clever individuals and manage complex systems. Since about forty years now, a couple of scientists have tried to mimic this process, and they have learned to exploit some tricks of life for solving an amazing variety of design and management tasks. This paper tries to give an overview of some recent applications as well as a summary of what we know about the general behavior of evolutionary algorithms
A directed mutation operator for real coded genetic algorithms
Copyright @ Springer-Verlag Berlin Heidelberg 2010.Developing directed mutation methods has been an interesting research topic to improve the performance of genetic algorithms (GAs) for function optimization. This paper introduces a directed mutation (DM) operator for GAs to explore promising areas in the search space. In this DM method, the statistics information regarding the fitness and distribution of individuals over intervals of each dimension is calculated according to the current population and is used to guide the mutation of an individual toward the neighboring interval that has the best statistics result in each dimension. Experiments are carried out to compare the proposed DM technique with an existing directed variation on a set of benchmark test problems. The experimental results show that the proposed DM operator achieves a better performance than the directed variation on most test problems
Electronic structure and total energy of interstitial hydrogen in iron: Tight binding models
An application of the tight binding approximation is presented for the
description of electronic structure and interatomic force in magnetic iron,
both pure and containing hydrogen impurities. We assess the simple canonical
d-band description in comparison to a non orthogonal model including s and d
bands. The transferability of our models is tested against known properties
including the segregation energies of hydrogen to vacancies and to surfaces of
iron. In many cases agreement is remarkably good, opening up the way to quantum
mechanical atomistic simulation of the effects of hydrogen on mechanical
properties
Evolving Symbolic Controllers
International audienceThe idea of symbolic controllers tries to bridge the gap between the top-down manual design of the controller architecture, as advocated in Brooks' subsumption architecture, and the bottom-up designer-free approach that is now standard within the Evolutionary Robotics community. The designer provides a set of elementary behavior, and evolution is given the goal of assembling them to solve complex tasks. Two experiments are presented, demonstrating the efficiency and showing the recursiveness of this approach. In particular, the sensitivity with respect to the proposed elementary behaviors, and the robustness w.r.t. generalization of the resulting controllers are studied in detail
Experimental Comparisons of Derivative Free Optimization Algorithms
In this paper, the performances of the quasi-Newton BFGS algorithm, the
NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution
Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm
Optimizers (PSO) are compared experimentally on benchmark functions reflecting
important challenges encountered in real-world optimization problems.
Dependence of the performances in the conditioning of the problem and
rotational invariance of the algorithms are in particular investigated.Comment: 8th International Symposium on Experimental Algorithms, Dortmund :
Germany (2009
Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
Evolution has produced an astonishing diversity of species, each filling a
different niche. Algorithms like MAP-Elites mimic this divergent evolutionary
process to find a set of behaviorally diverse but high-performing solutions,
called the elites. Our key insight is that species in nature often share a
surprisingly large part of their genome, in spite of occupying very different
niches; similarly, the elites are likely to be concentrated in a specific
"elite hypervolume" whose shape is defined by their common features. In this
paper, we first introduce the elite hypervolume concept and propose two metrics
to characterize it: the genotypic spread and the genotypic similarity. We then
introduce a new variation operator, called "directional variation", that
exploits interspecies (or inter-elites) correlations to accelerate the
MAP-Elites algorithm. We demonstrate the effectiveness of this operator in
three problems (a toy function, a redundant robotic arm, and a hexapod robot).Comment: In GECCO 201
Co-evolving Memetic Algorithms: Initial Investigations
This paper presents and examines the behaviour of a system whereby the rules governing local search within a Memetic Algorithm are co-evolved alongside the problem representation. We describe the rationale for such a system, and the implementation of a simple version in which the evolving rules are encoded as (condition:action) patterns applied to the problem representation, and are effectively self-adapted. We investigate the behaviour of the algorithm on a test suite of problems, and show significant performance improvements over a simple Genetic Algorithm, a Memetic Algorithm using a fixed neighbourhood function, and a similar Memetic Algorithm which uses random rules, i.e. with the learning mechanism disabled. Analysis of these results enables us to draw some conclusions about the way that even the simplified system is able to discover and exploit different forms of structure and regularities within the problems. We suggest that this “meta-learning” of problem features provides a means both of creating highly scaleable algorithms, and of capturing features of the solution space in an understandable form
Self-consistent multi-mode lasing theory for complex or random lasing media
A semiclassical theory of single and multi-mode lasing is derived for open
complex or random media using a self-consistent linear response formulation.
Unlike standard approaches which use closed cavity solutions to describe the
lasing modes, we introduce an appropriate discrete basis of functions which
describe also the intensity and angular emission pattern outside the cavity.
This constant flux (CF) basis is dictated by the Green function which arises
when formulating the steady state Maxwell-Bloch equations as a self-consistent
linear response problem. This basis is similar to the quasi-bound state basis
which is familiar in resonator theory and it obeys biorthogonality relations
with a set of dual functions. Within a single-pole approximation for the Green
function the lasing modes are proportional to these CF states and their
intensities and lasing frequencies are determined by a set of non-linear
equations. When a near threshold approximation is made to these equations a
generalized version of the Haken-Sauermann equations for multi-mode lasing is
obtained, appropriate for open cavities. Illustrative results from these
equations are given for single and few mode lasing states, for the case of
dielectric cavity lasers. The standard near threshold approximation is found to
be unreliable. Applications to wave-chaotic cavities and random lasers are
discussed.Comment: 18 pages, 9 figure
A tight binding model for water
We demonstrate for the first time a tight binding model for water
incorporating polarizable anions. A novel aspect is that we adopt a "ground up"
approach in that properties of the monomer and dimer only are fitted.
Subsequently we make predictions of the structure and properties of hexamer
clusters, ice-XI and liquid water. A particular feature, missing in current
tight binding and semiempirical hamiltonians, is that we reproduce the almost
two-fold increase in molecular dipole moment as clusters are built up towards
the limit of bulk liquid. We concentrate on properties of liquid water which
are very well rendered in comparison with experiment and published density
functional calculations. Finally we comment on the question of the contrasting
densities of water and ice which is central to an understanding of the
subtleties of the hydrogen bond
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