Generating droplets in two-dimensional Ising spin glasses by using matching algorithms

We study the behavior of droplets for two dimensional Ising spin glasses with Gaussian interactions. We use an exact matching algorithm which enables study of systems with linear dimension L up to 240, which is larger than is possible with other approaches. But the method only allows certain classes of droplets to be generated. We study single-bond, cross and a category of fixed volume droplets as well as first excitations. By comparison with similar or equivalent droplets generated in previous works, the advantages but also the limitations of this approach are revealed. In particular we have studied the scaling behavior of the droplet energies and droplet sizes. In most cases, a crossover of the data can be observed such that for large sizes the behavior is compatible with the one-exponent scenario of the droplet theory. Only for the case of first excitations, no clear conclusion can be reached, probably because even with the matching approach the accessible system sizes are still too small.Comment: 11 pages, 16 figures, revte

Ground states of two-dimensional ±\pmJ Edwards-Anderson spin glasses

We present an exact algorithm for finding all the ground states of the two-dimensional Edwards-Anderson $\pm J$ spin glass and characterize its performance. We investigate how the ground states change with increasing system size and and with increasing antiferromagnetic bond ratio $x$. We find that that some system properties have very large and strongly non-Gaussian variations between realizations.Comment: 15 pages, 21 figures, 2 tables, uses revtex4 macro

Ground-state behavior of the 3d +/-J random-bond Ising model

Large numbers of ground states of the three-dimensional $\pm J$ random-bond Ising model are calculated for sizes up to $14^3$ using a combination of a genetic algorithm and Cluster-Exact Approximation. Several quantities are calculated as function of the concentration $p$ of the antiferromagnetic bonds. The critical concentration where the ferromagnetic order disappears is determined using the Binder cumulant of the magnetization. A value of $p_c=0.222\pm 0.005$ is obtained. From the finite-size behavior of the Binder cumulant and the magnetization critical exponents $\nu=1.1 \pm 0.3$ and $\beta=0.2 \pm 0.1$ are calculated.Comment: 8 pages, 11 figures, revte

No spin-glass transition in the "mobile-bond" model

The recently introduced ``mobile-bond'' model for two-dimensional spin glasses is studied. The model is characterized by an annealing temperature T_q. On the basis of Monte Carlo simulations of small systems it has been claimed that this model exhibits a non-trivial spin-glass transition at finite temperature for small values of T_q. Here the model is studied by means of exact ground-state calculations of large systems up to N=256^2. The scaling of domain-wall energies is investigated as a function of the system size. For small values T_q<0.95 the system behaves like a (gauge-transformed) ferromagnet having a small fraction of frustrated plaquettes. For T_q>=0.95 the system behaves like the standard two-dimensional +-J spin-glass, i.e. it does NOT exhibit a phase transition at T>0.Comment: 4 pages, 5 figures, RevTe

From recombination of genes to the estimation of distributions: II. Continuous parameters

. The Breeder Genetic Algorithm (BGA) is based on the equation for the response to selection. In order to use this equation for prediction, the variance of the fitness of the population has to be estimated. For the usual sexual recombination this can be difficult. In this paper the new points (offspring) are generated from distributions, a uniform distribution and a distribution generated by univariate marginal distributions. For a class of unimodal fitness functions the performance of the BGA is analytically computed. The results are compared to gene recombination methods. The uniform distribution is approximately generated by line recombination; recombination methods acting independently on each gene approximate the second distribution. 1 Introduction The Breeder Genetic Algorithm (BGA) is based on the classical science of livestock breeding. The central part of this theory is the equation for the response to selection R(t) = b(t) \Delta I \Delta oe(t) (1) Here R denotes the respon..

From recombination of genes to the estimation of distributions I. binary parameters

Abstract. The Breeder Genetic Algorithm (BGA) is based on the equation for the response to selection. In order to use this equation for prediction, the variance of the tness of the population has to be estimated. For the usual sexual recombination this can be di cult. In this paper the new points (o spring) are generated from distributions, a uniform distribution and a distribution generated by univariate marginal distributions. For a class of unimodal tness functions the performance of the BGA is analytically computed. The results are compared to gene recombination methods. The uniform distribution is approximately generated by line recombination � recombination methods acting independently on each gene approximate the second distribution.