646 research outputs found
Evolutionary Computation in High Energy Physics
Evolutionary Computation is a branch of computer science with which,
traditionally, High Energy Physics has fewer connections. Its methods were
investigated in this field, mainly for data analysis tasks. These methods and
studies are, however, less known in the high energy physics community and this
motivated us to prepare this lecture. The lecture presents a general overview
of the main types of algorithms based on Evolutionary Computation, as well as a
review of their applications in High Energy Physics.Comment: Lecture presented at 2006 Inverted CERN School of Computing; to be
published in the school proceedings (CERN Yellow Report
Genetic algorithm dynamics on a rugged landscape
The genetic algorithm is an optimization procedure motivated by biological
evolution and is successfully applied to optimization problems in different
areas. A statistical mechanics model for its dynamics is proposed based on the
parent-child fitness correlation of the genetic operators, making it applicable
to general fitness landscapes. It is compared to a recent model based on a
maximum entropy ansatz. Finally it is applied to modeling the dynamics of a
genetic algorithm on the rugged fitness landscape of the NK model.Comment: 10 pages RevTeX, 4 figures PostScrip
A Study in function optimization with the breeder genetic algorithm
Optimization is concerned with the finding of global optima
(hence the name) of problems that can be cast in the form of a
function of several variables and constraints thereof. Among the
searching methods, {em Evolutionary Algorithms} have been shown to be
adaptable and general tools that have often outperformed traditional
{em ad hoc} methods. The {em Breeder Genetic Algorithm} (BGA)
combines a direct representation with a nice conceptual
simplicity. This work contains a general description of the algorithm
and a detailed study on a collection of function optimization
tasks. The results show that the BGA is a powerful and reliable
searching algorithm. The main discussion concerns the choice of
genetic operators and their parameters, among which the family of
Extended Intermediate Recombination (EIR) is shown to stand out. In
addition, a simple method to dynamically adjust the operator is
outlined and found to greatly improve on the already excellent overall
performance of the algorithm.Postprint (published version
Annealing schedule from population dynamics
We introduce a dynamical annealing schedule for population-based optimization
algorithms with mutation. On the basis of a statistical mechanics formulation
of the population dynamics, the mutation rate adapts to a value maximizing
expected rewards at each time step. Thereby, the mutation rate is eliminated as
a free parameter from the algorithm.Comment: 6 pages RevTeX, 4 figures PostScript; to be published in Phys. Rev.
Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings
While evolutionary algorithms are known to be very successful for a broad
range of applications, the algorithm designer is often left with many
algorithmic choices, for example, the size of the population, the mutation
rates, and the crossover rates of the algorithm. These parameters are known to
have a crucial influence on the optimization time, and thus need to be chosen
carefully, a task that often requires substantial efforts. Moreover, the
optimal parameters can change during the optimization process. It is therefore
of great interest to design mechanisms that dynamically choose best-possible
parameters. An example for such an update mechanism is the one-fifth success
rule for step-size adaption in evolutionary strategies. While in continuous
domains this principle is well understood also from a mathematical point of
view, no comparable theory is available for problems in discrete domains.
In this work we show that the one-fifth success rule can be effective also in
discrete settings. We regard the ~GA proposed in
[Doerr/Doerr/Ebel: From black-box complexity to designing new genetic
algorithms, TCS 2015]. We prove that if its population size is chosen according
to the one-fifth success rule then the expected optimization time on
\textsc{OneMax} is linear. This is better than what \emph{any} static
population size can achieve and is asymptotically optimal also among
all adaptive parameter choices.Comment: This is the full version of a paper that is to appear at GECCO 201
Integrated Design of Superconducting Magnets with the CERN Field Computation Program ROXIE
The program package ROXIE has been developed at CERN for the field computation of superconducting accelerator magnets and is used as an approach towards the integrated design of such magnets. It is also an example of fruitful international collaborations in software development.The integrated design of magnets includes feature based geometry generation, conceptual design using genetic optimization algorithms, optimization of the iron yoke (both in 2d and 3d) using deterministic methods, end-spacer design and inverse field calculation.The paper describes the version 8.0 of ROXIE which comprises an automatic mesh generator, an hysteresis model for the magnetization in superconducting filaments, the BEM-FEM coupling method for the 3d field calculation, a routine for the calculation of the peak temperature during a quench and neural network approximations of the objective function for the speed-up of optimization algorithms, amongst others.New results of the magnet design work for the LHC are given as examples
- …