Population-based Incremental Learning Algorithm Research Articles

Cant sawing log positioning in a sawmill: searching for an optimal solution

Automated three-dimensional log scanning and positioning systems in a sawmill have a limited time available to reach an optimal positioning solution before primary breakdown sawing starts. In this study a search algorithm (tentacle algorithm) was developed for this task and was empirically evaluated in terms of its ability to find an optimal or close-to-optimal positioning solution in a limited number of iterations. This algorithm was compared to the population-based incremental learning algorithm, the simulated annealing algorithm, and the particle swarm optimisation algorithm. The tentacle algorithm performed the best of all the algorithms evaluated in terms of the mean volume recovery obtained. However, exhaustive searches around the centred and ‘horns-up’ and ‘horns-down’ positions using smaller ranges resulted in better mean volume recovery results than any of the algorithms, although the mean results using this strategy did not differ significantly from that of the tentacle algorithm.

An evolutionary multi-objective optimization of trading rules in call markets

SUMMARY We evaluate an agent-based model featuring near-zero-intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices and which orders are executed, as well as a range of parameters regarding market intervention by market makers and the presence of informed traders. We optimize these trading rules using a multi-objective population-based incremental learning algorithm seeking to maximize the trading volume and minimize the bid–ask spread. Our results suggest that markets should choose a small tick size if concerns about the bid–ask spread are dominating and a large tick size if maximizing trading volume is the main aim. We also find that unless concerns about trading volume dominate, time priority is the optimal priority rule. Copyright © 2011 John Wiley & Sons, Ltd.

Determining the optimal market structure using near-zero intelligence traders

We evaluate an agent-based model featuring near-zero-intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices, which orders are executed as well as a range of parameters regarding market intervention by specialists and the presence of informed traders. We optimize these trading rules using a population-based incremental learning algorithm seeking to maximize the trading volume. Our results suggest markets should choose a large tick size and ensure only a small fraction of traders are informed about the order book. The effect of trading rules regarding the determination of prices, priority rules, and specialist intervention, we find to have an ambiguous effect on the outcome.

Chaotic synchronization using PID control combined with population based incremental learning algorithm

Evolutionary algorithms (EAs) refer to a broad class of optimization algorithms, which take some inspiration from evolutionary systems in the natural world. In recent years, a new class of EAs called estimation of distribution algorithms (EDAs) has emerged based on probabilistic modeling of the search space. Instances of EDAs include population based incremental learning (PBIL) algorithm. The PBIL algorithm is an easy to understand heuristic optimization technique that is inspired by the genetic algorithm and the competitive learning paradigm. PBIL includes many of the features from the genetic algorithm such as binary string representation, the notion of individuals, fitness measures and mutations. Contrary to the genetic algorithm it does not maintain a population of individuals but instead PBIL contains a probability vector. At each generation a new population of individuals is sampled according to the probabilities specified in the probability vector. The population is evaluated and the probability vector is updated by dragging it towards the best individual in the population. In recent years, the investigation of synchronization of chaotic systems has attracted much attention of researchers. Chaos synchronization has been applied in many fields such as secure communication, chemical, engineering, and biological systems, among others. This paper presents the synchronization of two identical discrete chaotic systems subject the different initial conditions by designing a proportional–integral–derivative (PID) controller. In addition, the tuning of the PID controller based on a modified PBIL algorithm using similarity analysis is also investigated in this paper. Simulation results show the good performance of the modified PBIL algorithm for synchronization of chaotic systems.

Tuning the PBIL algorithm to solve a real-world FAP problem

Frequency planning, also known as frequency assignment problem (FAP), is a very important task for current GSM operators. FAP basically tries to minimise the number of interferences (or conflicts in the communications) caused when a limited number of frequencies has to be assigned to a quite high number of transceivers (and there are much more transceivers than frequencies). In this work, we focus on solving this problem for a realistic-sized, real-world GSM network using the population-based incremental learning (PBIL) algorithm. The work described here is divided in two parts. In the first one, we analyse and fix the standard PBIL algorithm to solve the FAP; whereas in the second, we take as initial point the results obtained with the standard version of PBIL and we perform a complete study with the most relevant variations of the algorithm to discover which approach can compute the best frequency plans for real-world instances.

HPBILc: A histogram-based EDA for continuous optimization

Designing different estimation of distribution algorithms for continuous optimization is a recent emerging focus in the evolutionary computation field. This paper proposes an improved population-based incremental learning algorithm using histogram probabilistic model for continuous optimization. Histogram models are advantageous in describing the solution distribution of complex and multimodal continuous problems. The algorithm utilizes the sub-dividing strategy to guarantee the accuracy of optimal solutions. Experimental results show that the proposed algorithm is effective and it obtains better performance than the fast evolutionary programming (FEP) and those newly published EDAs in most test functions.

Evolutionary multi-objective optimization in robot soccer system for education

As the robot soccer system becomes stabilized, it has been used as an educational platform with which various topics on mobile robotics can be taught. As one of key topics in the education of mobile robotics is computational intelligence-based navigation, this paper proposes a multiobjective population-based incremental learning (MOPBIL) algorithm to obtain the fuzzy path planner for optimal path to the ball, minimizing three objectives such as elapsed time, heading direction and posture angle errors in a robot soccer system. MOPBIL employs the probabilistic mechanism, which generates new population using probability vectors. As the probability vectors are updated by referring to nondominated solutions, population converges to Pareto-optimal solution set. Simulation and experiment results show the effectiveness of the proposed MOPBIL from the viewpoint of the proximity to the Pareto-optimal set, size of the dominated space, coverage of two sets and diversity metric. By implementing each of the solutions into the educational platform, it can be educated how multi-objective optimization is realized in the real-world problem.

Population-Based Incremental Learning With Associative Memory for Dynamic Environments

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.

A diversity maintaining population-based incremental learning algorithm

In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposition as a means for controlling the amount of diversity within a given sample population. We prove that under this scheme we are able to asymptotically guarantee a higher diversity, which allows for a greater exploration of the search space. The presented probabilistic algorithm is specifically for applications in the binary domain. The benchmark data used for the experiments are commonly used deceptive and attractor basin functions as well as 10 common travelling salesman problem instances. Our experimental results focus on the effect of parameters and problem size on the accuracy of the algorithm as well as on a comparison to traditional population-based incremental learning. We show that the new algorithm is able to effectively utilize the increased diversity of opposition which leads to significantly improved results over traditional population-based incremental learning.

A Fast Scalable Evolutionary Algorithm for the QoS Multicast Routing Problem

Abstract The increasing demand of real-time multimedia services makes of quality of service based routing a serious challenge for next-generation networks. The complexity of this NP-complete problem significantly increases with the size of the network. A new evolutionazybased multicast routing algorithm is presented in this paper. It is based on computational intelligence techniques that integrate in an efficient manner the merits of genetic algorithms and the concepts of competitive leazning in the area of artificial neural networks. Population-based incremental learning algorithm is utilized, among other techniques, to construct a delay bounded multicast tree. The proposed algorithm is capable of simultaneously satisfying several key quality of service requirements that aze necessazy for real-time multimedia applications. The main objective of the algorithm is to construct a multicast tree that is characterized by a minimum cost and a bounded end-to-end delay and residual bandwidth. It is shown through ...

Remote sensing of a magnetic target utilizing population based incremental learning

Magnetic anomaly detection (MAD) systems are used for decades to detect obscured ferromagnetic targets. We propose the population based incremental learning (PBIL) approach for localization of a static ferromagnetic target, and estimation of its magnetic moment. The ferromagnetic target is assumed as a magnetic dipole, which produces an anomaly in the ambient Earth magnetic field. The magnetic field is measured either by a mobile magnetometer or by a network of static magnetic sensors. The measured signal is processed utilizing PBIL algorithm, in order to characterize the magnetic target, i.e. localize the target and estimate its magnetic moment. The method has been tested by numerous computer simulations, and showed promising results. For SNR larger than 0.2 the localization error is less than 10% relative to closest proximity approach (CPA), and the magnetic moment estimation error is less than 20%. Applying the proposed method to a magnetic dipole target signal on the background of a real world magnetic noise proved to be consistent with simulation results.

A New Implementation of Population Based Incremental Learning Method for Optimizations in Electromagnetics

To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus re-defined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm

POWER SYSTEM STABILIZER DESIGN FOR MULTIMACHINE POWER SYSTEM USING POPULATION-BASED INCREMENTAL LEARNING

This paper uses a novel simplified version of GAs called Population-Based Incremental Learning (PBIL) to optimally tune the parameters of the power system stabilizers (PSSs) for a multi-machine system. The technique combines aspects of GAs and competitive learning-based artificial neural network. The issue of optimally tuning the parameters of the PSS is converted into an optimization problem that is solved via the PBIL algorithm. Simulation results are presented to show the effectiveness of the proposed approach.

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

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 PBIL’s 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.

Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.