Gene regulatory network reverse engineering using population based incremental learning and K-means

Population-based incremental learning (PBIL) has been recently applied to a range of optimization problems in controller designs with promising results. It combines aspects of genetic algorithm with competitive learning. The learning rate in the standard PBIL is generally fixed which makes it difficult for the algorithm to explore the search space effectively. In this paper, the standard PBIL is improved by using a combination of adaptive and fixed learning rate that varies according to the generation. The adaptive-fixed (AF) algorithm can adjust the learning rate automatically according to the degree of evolution of the search. The objective of the power system stabilizer (PSS) design is to achieve adequate stability over a wide range of power system operating conditions. The proposed controller is compared with conventional PBIL with fixed learning rate (PBIL) and tested under various operating conditions. Simulation results show that the AFPBIL based PSS provides a more efficient search capability and gives a better damping and adequate dynamic performance of the system than the conventional PBIL based PSS.

Evolutionary algorithms (EAs) have become the standards and paradigms for solving inverse problems. However, their two inherited operations, namely, the crossover and mutation operations, are complicated and difficult, both in theory and in numerical implementations. In this regard, increasing efforts have been devoted to EAs which are based on probabilistic models (EAPMs) to overcome the shortcomings of available EAs. The population-based incremental learning (PBIL) is an EAPM; moreover, it can bridge the gap between machine learning and the EAs, hence enjoying several merits compared with other EAs. However, lukewarm efforts have been devoted to PBILs, especially the real coded PBILs, in the study of inverse problems in electromagnetics. In this regard, a novel real coded PBIL is being proposed in this paper. In the proposed real coded PBIL, a probability matrix is proposed to randomly generate a population, and the updating formulas for this probability matrix using the so far searched best solution and the best solution of the current population are introduced to strike a balance between convergence performance and solution quality. The proposed real coded PBIL algorithm is numerically experimented on several case studies and promising results are reported in this paper.

Performance evaluation of power system stabilizers based on Population-Based Incremental Learning (PBIL) algorithm

The population-based incremental learning (PBIL) is a novel evolutionary algorithm combined the mechanisms of the Genetic Algorithm with competitive learning. In this paper, the influence of the number of selected best solutions on the convergence speed of the PBIL is studied by experiment. Based on experimental results, a PBIL algorithm with elitist strategy, named Double Learning PBIL (DLPBIL), is proposed. The new algorithm learns both the selected best solutions in current population and the optimal solution found so far in the algorithm at same time. Experimental results show that the DLPBIL out-performs the standard PBIL. Both the convergence speed and the solution quality are improved.

In this paper we study two different meta-heuristics to solve a real-word frequency assignment problem (FAP) in GSM networks. We have used a precise mathematical formulation in which the frequency plans are evaluated using accurate interference information coming from a real GSM network. We have developed an improved version of the scatter search (SS) algorithm in order to solve this problem. After accurately tuning this algorithm, it has been compared with a version fixed for the FAP problem of the population-based incremental learning (PBIL) algorithm. The results show that SS obtains better frequency plannings than PBIL for all the experiments performed.

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.

This article deals with the comparative performance of some established multi-objective evolutionary algorithms (MOEAs) for structural topology optimization. Four multi-objective problems, having design objectives like structural compliance, natural frequency and mass, and subjected to constraints on stress, etc., are posed for performance testing. The MOEAs include Pareto archive evolution strategy (PAES), population-based incremental learning (PBIL), non-dominated sorting genetic algorithm (NSGA), strength Pareto evolutionary algorithm (SPEA), and multi-objective particle swarm optimization (MPSO). The various MOEAs are implemented to solve the problems. The ground element filtering (GEF) technique is used to suppress checkerboard patterns on topologies. The results obtained from the various optimizers are illustrated and compared. It is shown that PBIL is far superior to the others. The optimal topologies from using PBIL can be compared with those obtained by employing the classical gradient-based approach. It can be considered as a powerful tool for structural topological design.

On the efficiency of the binary flower pollination algorithm: Application on the antenna positioning problem

A suboptimal H/sub /spl infin// PSS tuning method has been developed. This method can improve overall damping factor and robustness by using the existing PSS structure. One of the optimization methods applied to this tuning procedure was sequential quadratic programming (SQP). Since SQP only deals with convex problems, it is not suitable for multi-machine power systems. To address the shortcomings of the method an optimization method called PBIL (population based incremental learning) with the key features of its ability to optimize nonconvex, multi-variable problems has been applied to the tuning method developed.

Revealing gene regulation-based neural network computing in bacteria

In order to facilitate real-time monitoring of accumulated wellhead fatigue damage, it is necessary to measure the wellhead bending moment in real-time. This paper presents a novel method to estimate the wellhead bending moment in realtime using acceleration and inclination data from the motion reference unit (MRU) sensors installed on BOP and LRJ, riser tension data and a trained neural network model. The method proposed in this paper is designed with a Recursive Neural Network (RNN) model to be trained to estimate the wellhead bending moment in real-time with high accuracy based on motion MRU sensor data and riser tension time series of a few previous cycles. In addition to the power of modeling complex nonlinearities, RNNs provide the advantage of better capturing the dynamic effects by learning to recognize the patterns in the sensor data and riser tension time series. The RNN model is trained using virtual sensor data and wellhead bending moment from a finite element (FE) model of the drilling riser subjected to irregular wave time domain analyses based on a training matrix with limited number of significant height (Hs) and peak period (Tp) combinations. Once trained, tested and deployed, the RNN model can make real-time estimation of the wellhead bending moment based on MRU sensor data and riser tension time series. The RNN model can be an efficient and accurate alternative to a physical model based on the indirect method for real-time calculation of wellhead bending moment using real-time sensor data. A case study is presented to explain the training procedures for the RNN model. A set of test cases that are not included in the training dataset are used to demonstrate the accuracy of the RNN model using Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE) and coefficient of determination (R2) as a metrics.

An alternative to Darwinian-like artificial evolution is offered by Population-Based Incremental Learning (PBIL): this algorithm memorizes the best past individuals and uses this memory as a distribution, to generate the next population from scratch. This paper extends PBIL from boolean to continuous search spaces. A Gaussian model is used for the distribution of the population. The center of this model is constructed as in boolean PBIL. Several ways of defining and adjusting the variance of the model are investigated. The approach is validated on several large-sized problems.

Variable selection is the problem of choosing the subset of explanatory variables for a regression or classification model such that the resulting model is best according to some criterion. Here we consider the use of population-based incremental learning (PBIL) to select the variables for a linear regression model to predict a quantitative trait in living organisms. The data here is simulated to represent a genome-wide association study (GWAS) using single nucleotide polymorphisms (SNPs) as explanatory variables and height as an example trait. PBIL was effective in optimizing a variety of model fitness criteria. The resulting models were found to have true positive and false negative rates comparable to those of competing methods.

Recently, genetic algorithms (GAs) have attracted great interest owing to efficiency and flexibility against complex robot path planning problems. To accelerate the convergence speed, preceding researches adapted conventional GAs by using problem-specific techniques. However, such approaches increase computational burden and algorithmic complexity, resulting in subsequent additional problems. In this paper, we used Population-Based Incremental Learning (PBIL) algorithm for robot path planning as a probabilistic evolutionary approach. In addition to PBIL, we also proposed the probabilistic model of nodes and the edge bank to generate promising paths. The experimental results demonstrate that the proposed method gave markedly better performance than its conventional counter-parts(GA,kGA,fGA) in terms of success rates and the quality of obtained paths.

Relay communication networks increase the spectral efficiency and remote availability with the support of Multiple-Input Multiple-Output (MIMO). Channel estimation in the MIMO relay communication networks is mandatory to enhance the represented capabilities. Hence, developing channel estimation approaches in MIMO relay communication networks is one of the noteworthy aspects of recent researches. Besides, compressed sensing (CS) as a spectral efficient signal processing approach is attractive for estimators. Hence, in this paper, we have used CS based channel estimation method to improve the accuracy and spectral efficiency of channel estimation for MIMO-OFDM relay network. As represented, having a proper pilot allocation will increase the efficiency, and to this end, we are encountered with a combinatorial optimization which is dealt with Geometrical Differential Evolution (GDE) and Population-Based Incremental Learning (PBIL) in this paper. These are population-based evolutionary optimization. GDE is utilized for its speed and accuracy of convergence. Besides, PBIL is used for its probability nature which makes it capable of escaping from local minimum trapping. The illustrated result of simulations represents the superiority of the proposed methods in defining the pilot allocation sequences.