13,681 research outputs found
Searching the solution space in constructive geometric constraint solving with genetic algorithms
Geometric problems defined by constraints have an exponential number
of solution instances in the number of geometric elements involved.
Generally, the user is only interested in one instance such that
besides fulfilling the geometric constraints, exhibits some additional
properties.
Selecting a solution instance amounts to selecting a given root every
time the geometric constraint solver needs to compute the zeros of a
multi valuated function. The problem of selecting a given root is
known as the Root Identification Problem.
In this paper we present a new technique to solve the root
identification problem. The technique is based on an automatic search
in the space of solutions performed by a genetic algorithm. The user
specifies the solution of interest by defining a set of additional
constraints on the geometric elements which drive the search of the
genetic algorithm. The method is extended with a sequential niche
technique to compute multiple solutions. A number of case studies
illustrate the performance of the method.Postprint (published version
Hierarchical Crossover and Probability Landscapes of Genetic Operators
The time evolution of a simple model for crossover is discussed. A variant of
this model with an improved exploration behavior in phase space is derived as a
subset of standard one- and multi-point crossover operations. This model is
solved analytically in the flat fitness case. Numerical simulations compare the
way of phase space exploration of different genetic operators. In the case of a
non-flat fitness landscape, numerical solutions of the evolution equations
point out ways to estimate premature convergence.Comment: 11 pages, uuencoded postcript fil
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RGFGA: An efficient representation and crossover for grouping genetic algorithms
There is substantial research into genetic algorithms that are used to group large numbers of
objects into mutually exclusive subsets based upon some fitness function. However, nearly all
methods involve degeneracy to some degree.
We introduce a new representation for grouping genetic algorithms, the restricted growth function
genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman
optimisation method and a well-established statistical clustering algorithm, with encouraging results
Group Leaders Optimization Algorithm
We present a new global optimization algorithm in which the influence of the
leaders in social groups is used as an inspiration for the evolutionary
technique which is designed into a group architecture. To demonstrate the
efficiency of the method, a standard suite of single and multidimensional
optimization functions along with the energies and the geometric structures of
Lennard-Jones clusters are given as well as the application of the algorithm on
quantum circuit design problems. We show that as an improvement over previous
methods, the algorithm scales as N^2.5 for the Lennard-Jones clusters of
N-particles. In addition, an efficient circuit design is shown for two qubit
Grover search algorithm which is a quantum algorithm providing quadratic
speed-up over the classical counterpart
Population Synthesis via k-Nearest Neighbor Crossover Kernel
The recent development of multi-agent simulations brings about a need for
population synthesis. It is a task of reconstructing the entire population from
a sampling survey of limited size (1% or so), supplying the initial conditions
from which simulations begin. This paper presents a new kernel density
estimator for this task. Our method is an analogue of the classical
Breiman-Meisel-Purcell estimator, but employs novel techniques that harness the
huge degree of freedom which is required to model high-dimensional nonlinearly
correlated datasets: the crossover kernel, the k-nearest neighbor restriction
of the kernel construction set and the bagging of kernels. The performance as a
statistical estimator is examined through real and synthetic datasets. We
provide an "optimization-free" parameter selection rule for our method, a
theory of how our method works and a computational cost analysis. To
demonstrate the usefulness as a population synthesizer, our method is applied
to a household synthesis task for an urban micro-simulator.Comment: 10 pages, 4 figures, IEEE International Conference on Data Mining
(ICDM) 201
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