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

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

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

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

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

Steady-state MreB helices inside bacteria: dynamics without motors

Within individual bacteria, we combine force-dependent polymerization dynamics of individual MreB protofilaments with an elastic model of protofilament bundles buckled into helical configurations. We use variational techniques and stochastic simulations to relate the pitch of the MreB helix, the total abundance of MreB, and the number of protofilaments. By comparing our simulations with mean-field calculations, we find that stochastic fluctuations are significant. We examine the quasi-static evolution of the helical pitch with cell growth, as well as timescales of helix turnover and denovo establishment. We find that while the body of a polarized MreB helix treadmills towards its slow-growing end, the fast-growing tips of laterally associated protofilaments move towards the opposite fast-growing end of the MreB helix. This offers a possible mechanism for targeted polar localization without cytoplasmic motor proteins.Comment: 7 figures, 1 tabl

Phase equilibrium in two orbital model under magnetic field

The phase equilibrium in manganites under magnetic field is studied using a two orbital model, based on the equivalent chemical potential principle for the competitive phases. We focus on the magnetic field induced melting process of CE phase in half-doped manganites. It is predicted that the homogenous CE phase begins to decompose into coexisting ferromagnetic phase and CE phase once the magnetic field exceeds the threshold field. In a more quantitative way, the volume fractions of the two competitive phases in the phase separation regime are evaluated.Comment: 4 pages, 4 figure

Influence of an externally modulated photonic link on a microwave communications system

We analyze the influence of an externally modulated photonic link on the performance of a microwave communications system. From the analysis, we deduce limitations on the photocurrent, magnitude of the relaxation oscillation noise of the laser, third-order intercept point of the preamplifier, and other parameters in order for the photonic link to function according to the system specifications. Based on this, we outline a procedure for designing a photonic link that can be integrated in a system with minimal performance degradation

Alternative jump variables as surrogate measures of weightlifting performance in national weightlifters

Surrogate measures of weightlifting performance (WLp) have previously been monitored using loaded and unloaded squat jumps (SJ) (Bazyler et al 2018; Travis et al 2018; Carlock et al 2004) and countermovement jumps (CMJ) (Carlock et al 2004). Typically measures of jump height (JH) (Travis et al 2018; Carlock et al 2004) and peak power (PP) (Carlock et al 2004), and their associated scaled counterparts (Travis et al 2018; Carlock et al 2004) have been used to identify relationships between them and WLp. While these variables have been shown to have moderate to strong relationships (r = 0.60-0.93) (Carlock et al 2004), further information into the underpinning mechanisms that contribute to such outcome measures has not yet been investigated and may provide insight into variables more sensitive to change; thus, owing to the continued monitoring process