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About 473 papers for “Population-based incremental learning”

An Improved Population-Based Incremental Learning Algorithm

Komla A. Folly · International Journal of Swarm Intelligence Research · 2013

Population-Based Incremental Learning (PBIL) is a relatively new class of Evolutionary Algorithms (EA) that has been recently applied to a range of optimization problems in engineering with promising results. PBIL 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, a PBIL with adapting learning rate is proposed. The Adaptive PBIL (APBIL) is able to thoroughly explore the search space at the start of the run and maintain the diversity consistently during the run longer than the standard PBIL. The proposed algorithm is validated by applying it to power system controller parameters optimization problem. Simulation results show that the Adaptive PBIL based controller performs better than the standard PBIL based controller, in particular under small disturbance.

Cited by 7

Extending Population Based Incremental Learning using Dirichlet Processes

Leon Palafox, Nasimul Noman, Hitoshi Iba · 2013

The unimodal Gaussian has been the distribution of choice for many extensions in Estimation of Distribution Algorithms (EDA). Some groups have used clustering algorithms, like k-means, to use multimodal distributions in different modifications of EDA. Most proposals use a fixed number of groups or clusters, and other works use heuristic approaches to find the right number of clusters in the search space without any previous information. The heuristic methods, however, lack the mathematical rigor required in the inference of a probability distribution's parameters. In this work, we propose the use of the Nonparametric Bayesian Model known as Dirichlet Process to fit the number of clusters given the data in a modified Population Based Incremental Learning (PBIL) model. We compare our approach with similar techniques that also use multimodal probability distributions to enhance the quality of the search in other EDA approaches. Our approach shows improvements by reducing the number of generations needed to find good results that are comparable to the state of the art in clustered EDA.

Cited by 3

Breaking Knapsack Cipher Using Population Based Incremental Learning

Nasreen J. Kadhim · Journal of Al-Nahrain University-Science · 2011

The demand for effective internet security is increasing exponentially day by day. Businesses have an obligation to protect sensitive data from loss or theft. Such sensitive data can be potentially damaging if it is altered, destroyed or if it falls into the wrong hands. So they need to develop a scheme that guarantees to protect the information from attacker. Cryptology which is the science and study of systems for secret communication is at the heart of providing such guarantee. Breaking knapsack cipher using Population Based Incremental Learning (PBIL) is suggested in this paper. Results of this implementation are compared with results of Poonam Grag, Aditya Shastri, and D.C. The result of comparison shows that Population Based Incremental Learning has less complexity and enjoys high speed (i.e. correct results will be generated with less number of generation compared to that generated using genetic algorithm) also the overhead for genetic algorithm operations is significantly higher than for Population Based Incremental Learning.

Cited by 1Open access

Conformational searching using a population‐based incremental learning algorithm

S.M. Long, Tran T. Tran, Peter D. Adams et al. · Journal of Computational Chemistry · 2011

A new population-based incremental learning algorithm for conformational searching of molecules is presented. This algorithm is particularly effective at determining, by relatively small number of energy minimizations, global energy minima of large flexible molecules. The algorithm is also able to find a large set of low energy conformations of more rigid small molecules. The performance of the algorithm is relation to other algorithm is examined via the test molecules: C(18) H(38) , C(39)H(80) , cycloheptadecane and a set of five drug-like molecules.

Cited by 8

Function Optimisation using Multiple-Base Population Based Incremental Learning

M.P. Servais, G. de Jager, J. R. Greene · 1997

Population Based Incremental Learning (PBIL) is a stochastic search technique which combines characteristics of both the Genetic Algorithm and competitive learning. It has been shown to be a simple, yet widely effective function optimisation strategy. Applications of PBIL to date include: selecting the weights in a neural network, image registration, filter design and feature set extraction. A generalisation of PBIL to Base-N PBIL is presented and discussed. Following this, a Multiple Base version of PBIL (MB-PBIL) is presented, which attempts to optimise a function by searching in a variety of bases. (Similar strategies have been shown to be effective for hill-climbers.) Finally, it is shown that for the standard test functions used, MB-PBIL performed better than the standard PBIL. I. Introduction P OPULATION Based Incremental Learning (PBIL) was introduced by Baluja in 1994 [1]. It is a function optimisation technique which combines characteristics of both the Genetic Algorithm (GA...

Cited by 0

Population-Based Incremental Learning to Solve the FAP Problem

José M. Chaves-González, Miguel A. Vega‐Rodríguez, David Domínguez-González et al. · 2008

Frequency assignment problem (FAP) is a very important issue in the field of telecommunications (especially in GSM–Global System for Mobile–Networks). In this work, we present the Population-Based Incremental Learning (PBIL) algorithm to solve a particular branch of the FAP problem (MS-FAP). MS-FAP (Minimum Span Frequency Assignment Problem) tries to minimize the range of frequencies which is necessary in a certain area to cover the communications which take place there. In this paper it is presented the problem and it is explained the methodology which solve it. We have performed tests with a complete set of experiments using seven well known variations of PBIL and 7 types of MS-FAP problems. At the end, the results are presented and we compare them to conclude which variation of PBIL provides the best solution to the MS-FAP problem.

Cited by 5

A Population-Based Incremental Learning Algorithm with Elitist Strategy

Qingbin Zhang, WU Ti-hua, Bo Liu · 2007

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.

Cited by 15

Level-Based Analysis of the Population-Based Incremental Learning\n Algorithm

Per Kristian Lehre, Phan Trung Hai Nguyen · arXiv (Cornell University) · 2018

The Population-Based Incremental Learning (PBIL) algorithm uses a convex\ncombination of the current model and the empirical model to construct the next\nmodel, which is then sampled to generate offspring. The Univariate Marginal\nDistribution Algorithm (UMDA) is a special case of the PBIL, where the current\nmodel is ignored. Dang and Lehre (GECCO 2015) showed that UMDA can optimise\nLeadingOnes efficiently. The question still remained open if the PBIL performs\nequally well. Here, by applying the level-based theorem in addition to\nDvoretzky--Kiefer--Wolfowitz inequality, we show that the PBIL optimises\nfunction LeadingOnes in expected time $\\mathcal{O}(n\\lambda \\log \\lambda +\nn^2)$ for a population size $\\lambda = \\Omega(\\log n)$, which matches the bound\nof the UMDA. Finally, we show that the result carries over to BinVal, giving\nthe fist runtime result for the PBIL on the BinVal problem.\n

Cited by 0Open access

A note on the population based incremental learning with infinite population size

Reza Rastegar, M. R. Meybodi · 2005

In this paper, we study the dynamical properties of the population based incremental learning (PBIL) algorithm when it uses truncation, proportional, and Boltzmann selection schemas. The results show that if the population size tends to infinity, with any learning rate, the local optima of the function to be optimized are asymptotically stable fixed points of the PBIL

Cited by 5

Optimizing Curriculum Scheduling Problem Using Population Based Incremental Learning Algorithm

Qun Jiang, Yang Ou, Dong Shi-Du · 2007

This paper presents a methodology for using heuristic search methods to optimize scheduling problem. Specifically, an estimation of distribution algorithm (EDA) - population based incremental learning (PBIL), has been applied to find effective arrangement of curriculum schedule of Universities. To our knowledge, EDAs have been applied to fewer real world problems compared to GAs, and the aim of the present paper is to expand the application domain of this technique. The experimental results indicate a successful application of PBIL to optimize curriculum scheduling problem.

Cited by 0

Population Based Incremental Learning Versus Genetic Algorithms: Iterated Prisoners Dilemma

Timothy Gosling Nanlin, Nanlin Jin, Edward Tsang et al. · 2004

Axelrod's originally experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that while PBIL performs well, GA in general does slightly better although more experiments should be conducted.

Cited by 0

Population Based Incremental Learning Versus Genetic Algorithms: Iterated Prisoners Dilemma

Timothy Gosling, Nanlin Jin, Edward Tsang · Northumbria Research Link (Northumbria University) · 2004

Axelrod’s originally experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that while PBIL performs well, GA in general does slightly better although more experiments should be conducted

Cited by 19

Population-Based Incremental Learning With Associative Memory for Dynamic Environments

Shengxiang Yang, Xin Yao · IEEE Transactions on Evolutionary Computation · 2008

In recent years, interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) has grown 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 DOPs. 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, multipopulation, 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, multipopulation schemes for PBILs in different dynamic environments.

Cited by 258

A Population-Based Incremental Learning Method for Constrained Portfolio Optimisation

Yan Jin, Rong Qu, Jason Atkin · 2014

This paper investigates a hybrid algorithm which utilizes exact and heuristic methods to optimise asset selection and capital allocation in portfolio optimisation. The proposed method is composed of a customised population based incremental learning procedure and a mathematical programming application. It is based on the standard Markowitz model with additional practical constraints such as cardinality on the number of assets and quantity of the allocated capital. Computational experiments have been conducted and analysis has demonstrated the performance and effectiveness of the proposed approach.

Cited by 11

Antioptimisation of Trusses Using Two-Level Population-Based Incremental Learning

Phinit Tontragunrat, Sujin Bureerat · Journal of Applied Mathematics · 2013

Practical optimum design of structures often involves parameters with uncertainties. There have been several ways to deal with such optimisation problems, and one of the approaches is an antioptimisation process. The task is to find the optimum solution of common design variables while simultaneously searching for the worst case scenario of those parameters with uncertainties. This paper proposed a metaheuristic based on population-based incremental learning (PBIL) for solving antioptimisation of trusses. The new algorithm is called two-level PBIL which consists of outer and inner loops. Five antioptimisation problems are posed to test the optimiser performance. The numerical results show that the concept of using PBIL probability vectors for handling the antioptimisation of truss is powerful and effective. The two-level PBIL can be considered a powerful optimiser for antioptimisation of trusses.

Cited by 1Open access