Population-based incremental learning with associative memory for dynamic environments

Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Evolutionary Computation. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In recent years there has been a growing interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) due to its importance in real world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPss. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multi-population, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multi-population schemes for PBILs in different dynamic environments

Experimental study on population-based incremental learning algorithms for dynamic optimization problems

Copyright @ Springer-Verlag 2005.Evolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms. In this paper, the application of population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic problems is investigated. Inspired by the complementarity mechanism in nature a Dual PBIL is proposed, which operates on two probability vectors that are dual to each other with respect to the central point in the genotype space. A diversity maintaining technique of combining the central probability vector into PBIL is also proposed to improve PBILs adaptability in dynamic environments. In this paper, a new dynamic problem generator that can create required dynamics from any binary-encoded stationary problem is also formalized. Using this generator, a series of dynamic problems were systematically constructed from several benchmark stationary problems and an experimental study was carried out to compare the performance of several PBIL algorithms and two variants of standard genetic algorithm. Based on the experimental results, we carried out algorithm performance analysis regarding the weakness and strength of studied PBIL algorithms and identified several potential improvements to PBIL for dynamic optimization problems.This work was was supported by UK EPSRC under Grant GR/S79718/01

Dual population-based incremental learning for problem optimization in dynamic environments

Copyright @ 2003 Asia Pacific Symposium on Intelligent and Evolutionary SystemsIn recent years there is a growing interest in the research of evolutionary algorithms for dynamic optimization problems since real world problems are usually dynamic, which presents serious challenges to traditional evolutionary algorithms. In this paper, we investigate the application of Population-Based Incremental Learning (PBIL) algorithms, a class of evolutionary algorithms, for problem optimization under dynamic environments. Inspired by the complementarity mechanism in nature, we propose a Dual PBIL that operates on two probability vectors that are dual to each other with respect to the central point in the search space. Using a dynamic problem generating technique we generate a series of dynamic knapsack problems from a randomly generated stationary knapsack problem and carry out experimental study comparing the performance of investigated PBILs and one traditional genetic algorithm. Experimental results show that the introduction of dualism into PBIL improves its adaptability under dynamic environments, especially when the environment is subject to significant changes in the sense of genotype space

Influence of low-level Pr substitution on the superconducting properties of YBa2Cu3O7-delta single crystals

We report on measurements on Y1-xPrxBa2Cu3O7-delta single crystals, with x varying from 0 to 2.4%. The upper and the lower critical fields, Hc2 and Hc1, the Ginzburg-Landau parameter and the critical current density, Jc(B), were determined from magnetization measurements and the effective media approach scaling method. We present the influence of Pr substitution on the pinning force density as well as on the trapped field profiles analyzed by Hall probe scanning.Comment: 4 pages, 5 figures, accepted for publication in J. Phys. Conf. Se

In-plane thermal conductivity of large single crystals of Sm-substituted (Y1−x_{1-x}Smx_{x})Ba2_{2}Cu3_{3}O7−δ_{7-\delta}

We have investigated the in-plane thermal conductivity $\kappa_{ab}(T,H)$ of large single crystals of optimally oxygen-doped (Y$_{1-x}$,Sm$_{x}$)Ba$_{2}$Cu$_{3}$O$_{7-\delta}$ ($x$=0, 0.1, 0.2 and 1.0) and YBa$_{2}$(Cu$_{1-y}$Zn$_{y}$)$_{3}$O$_{7-\delta}$($y$=0.0071) as functions of temperature and magnetic field (along the c axis). For comparison, the temperature dependence of $\kappa_{ab}$ for as-grown crystals with the corresponding compositions are presented. The nonlinear field dependence of $\kappa_{ab}$ for all crystals was observed at relatively low fields near a half of $T_{c}$. We make fits of the $\kappa(H)$ data to an electron contribution model, providing both the mean free path of quasiparticles $\ell_{0}$ and the electronic thermal conductivity $\kappa_{e}$, in the absence of field. The local lattice distortion due to the Sm substitution for Y suppresses both the phonon and electron contributions. On the other hand, the light Zn doping into the CuO $_{2}$ planes affects solely the electron component below $T_{c}$, resulting in a substantial decrease in $\ell_{0}$ .Comment: 7 pages,4 figures,1 tabl

Universal Scaling of the Neel Temperature of Near-Quantum-Critical Quasi-Two-Dimensional Heisenberg Antiferromagnets

We use a quantum Monte Carlo method to calculate the Neel temperature T_N of weakly coupled S=1/2 Heisenberg antiferromagnetic layers consisting of coupled ladders. This system can be tuned to different two-dimensional scaling regimes for T > T_N. In a single-layer mean-field theory, \chi_s^{2D}(T_N)=(z_2J')^{-1}, where \chi_s^{2D} is the exact staggered susceptibility of an isolated layer, J' the inter-layer coupling, and z_2=2 the layer coordination number. With a renormalized z_2, we find that this relationship applies not only in the renormalized-classical regime, as shown previously, but also in the quantum-critical regime and part of the quantum-disordered regime. The renormalization is nearly constant; k_2 ~ 0.65-0.70. We also study other universal scaling functions.Comment: 4 pages, 4 figure

Magnetic Excitations of Stripes and Checkerboards in the Cuprates

We discuss the magnetic excitations of well-ordered stripe and checkerboard phases, including the high energy magnetic excitations of recent interest and possible connections to the "resonance peak" in cuprate superconductors. Using a suitably parametrized Heisenberg model and spin wave theory, we study a variety of magnetically ordered configurations, including vertical and diagonal site- and bond-centered stripes and simple checkerboards. We calculate the expected neutron scattering intensities as a function of energy and momentum. At zero frequency, the satellite peaks of even square-wave stripes are suppressed by as much as a factor of 34 below the intensity of the main incommensurate peaks. We further find that at low energy, spin wave cones may not always be resolvable experimentally. Rather, the intensity as a function of position around the cone depends strongly on the coupling across the stripe domain walls. At intermediate energy, we find a saddlepoint at $(\pi,\pi)$ for a range of couplings, and discuss its possible connection to the "resonance peak" observed in neutron scattering experiments on cuprate superconductors. At high energy, various structures are possible as a function of coupling strength and configuration, including a high energy square-shaped continuum originally attributed to the quantum excitations of spin ladders. On the other hand, we find that simple checkerboard patterns are inconsistent with experimental results from neutron scattering.Comment: 11 pages, 13 figures, for high-res figs, see http://physics.bu.edu/~yaodx/spinwave2/spinw2.htm

Magnetic Excitations of Stripes Near a Quantum Critical Point

We calculate the dynamical spin structure factor of spin waves for weakly coupled stripes. At low energy, the spin wave cone intensity is strongly peaked on the inner branches. As energy is increased, there is a saddlepoint followed by a square-shaped continuum rotated 45 degree from the low energy peaks. This is reminiscent of recent high energy neutron scattering data on the cuprates. The similarity at high energy between this semiclassical treatment and quantum fluctuations in spin ladders may be attributed to the proximity of a quantum critical point with a small critical exponent $\eta$.Comment: 4+ pages, 5 figures, published versio