Empirical Analysis of ideal recombination on random decomposable problems

Jul 14, 20071 like632 views

This document presents an empirical analysis of ideal crossover strategies within random additively decomposable problems (RADPs) using genetic algorithms (GAs). It explores population sizing, run duration, and the scaling behavior of selectorecombinative GAs while verifying the applicability of previously developed facetwise models based on adversarial test problems. Key findings indicate that GAs exhibit subquadratic scalability with problem size and differentiate between easy and hard problem instances based on signal variance characteristics.

Economy & Finance Technology

Empirical Analysis of ideal recombination on random decomposable problems

The document describes research into using efficient estimation of distribution algorithms (EDAs) like the compact genetic algorithm (cGA) to solve optimization problems involving billions of variables. Key aspects discussed include the cGA's memory and computational efficiency through techniques like parallelization and vectorization. The researchers were able to solve a noisy OneMax problem involving over 33 million variables to optimality and a problem with 1.1 billion variables with relaxed convergence. The document argues this research is important because many real-world problems involving nanotechnology, biology, and information systems require solving optimization problems at massive scales.

Ad24210214IJERA Editor

This document summarizes a technique called CADU (collaborative adaptive down-sampling and upconversion) to improve image compression at low bit rates. The technique adaptively decreases high frequency information by directionally prefiltering an image before uniform downsampling. This allows the downsampled image to be conventionally compressed while avoiding aliasing artifacts. At the decoder, the low-resolution image is decompressed and then upconverted to the original resolution using constrained least squares restoration with an autoregressive model. Experimental results show CADU outperforms JPEG2000 in PSNR and visual quality at low to medium bit rates. The technique suggests oversampling wastes resources and could hurt quality given tight bit budgets.

Development of Reliability Analysis and Multidisciplinary Design Optimization...Altair

This document summarizes the capabilities of the RAMDO software for reliability-based design optimization (RBDO). It discusses both sensitivity-based and sampling-based RBDO approaches in RAMDO. It also provides examples of multidisciplinary applications of RAMDO in areas like fatigue analysis, casting process design, vehicle crashworthiness, and more. Several published case studies demonstrate how RAMDO has been used to optimize designs while accounting for input variability and reliability constraints.

1 icom 2001Isabault

This study examined two hypotheses about what drives performance on working memory span tasks: the resource-sharing hypothesis and the temporal decay hypothesis. The researchers developed a new paradigm to test these hypotheses. In one experiment, they found that equating duration while manipulating cognitive cost did not significantly change span performance across tasks. A second experiment showed that a continuous operation span task requiring sustained attention had a lower span than a reading operation span task. This supports the idea that working memory span depends more on the attentional demands of the processing task than on duration alone.

3 escop 2005Isabault

1. The document summarizes the Time-Based Resource-Sharing model of working memory which proposes that working memory span tasks depend only on the time during which processing captures attention, not on the complexity of the processing. 2. According to the model, working memory performance depends on the rate of processing (number of retrievals per unit time), the duration of individual processing steps, and whether processing involves retrievals from long-term memory. 3. Several experiments are described that support the model by showing equivalent effects of different tasks when their actual processing durations are equated.

4 icwm 2004Isabault

The document summarizes the time-based resource-sharing model of working memory. The model proposes that (1) processing and memory maintenance both require attention, which is limited, requiring sharing. (2) When attention is switched away from maintenance, activation decays over time. (3) Memory retrievals have the most detrimental effect on concurrent maintenance due to a bottleneck. (4) Sharing attention is time-based, with cognitive load determined by the proportion of time attention is captured by processing versus refreshing memory. Experiments support cognitive load varying with retrieval rate and duration rather than complexity.

presentationjie ren

This document summarizes quantization design techniques including Lloyd-Max quantizers and variable rate optimum quantizers. It discusses the problem setup for scalar quantization and outlines the local optimality conditions, alternating optimization approach, and dynamic programming approach for designing Lloyd-Max quantizers. It also covers the problem setup for variable rate optimum quantizer design subject to an entropy constraint, and describes analyzing this using a generalized Lloyd-Max algorithm.

R-NSGAIIpaskorn

The document proposes an approach called R-NSGA-II that uses reference points to guide an evolutionary multi-objective optimization algorithm towards regions of interest identified by a decision maker. It modifies NSGA-II, a popular multi-objective evolutionary algorithm, to focus on finding Pareto optimal solutions close to user-provided reference points. The crowding distance metric is changed to measure distance from solutions to reference points instead of from other solutions. This helps produce a set of non-dominated solutions near the regions specified as interesting by the decision maker.

Intelligent Bias of Network Structures in the Hierarchical BOAMartin Pelikan

This document describes research on intelligently biasing the network structures learned by the hierarchical Bayesian Optimization Algorithm (hBOA) to improve its performance. The researchers developed a method called split probability matrix (SPM) biasing, which uses prior information stored in an SPM to bias hBOA's network building process towards structures with a certain number of splits. They tested SPM biasing on two problems: Trap-5 and 2D Ising spin glasses. The experiments showed that SPM biasing significantly reduced hBOA's execution time, number of evaluations, and bits examined on both problems, with the best speedups achieved around a tuning parameter κ value of 1.

Using Previous Models to Bias Structural Learning in the Hierarchical BOAMartin Pelikan

Simplified Runtime Analysis of Estimation of Distribution AlgorithmsPer Kristian Lehre

The document discusses runtime analysis for Estimation of Distribution Algorithms (EDAs), particularly the Univariate Marginal Distribution Algorithm (UMDA), focusing on their performance in optimizing functions like LeadingOnes and OneMax. It reviews previous research and presents a level-based analysis that provides asymptotic expressions for expected fitness evaluations based on problem characteristics and algorithm settings. The findings suggest that the level-based method is effective for runtime analysis, indicating potential improvements over traditional approaches.

Spurious Dependencies and EDA ScalabilityMartin Pelikan

The document examines the impact of spurious dependencies in estimation of distribution algorithms (EDAs) on model accuracy, population sizing, and the number of generations required for convergence. It presents theoretical and empirical studies, highlighting how spurious dependencies can lead to increased population size requirements and potentially diminish the effectiveness of variation. The research also outlines empirical experiments on EDA performance regarding various population sizes and dependency structures.

Efficiency Enhancement of Estimation of Distribution AlgorithmsMartin Pelikan

This document discusses enhancing the efficiency of estimation of distribution algorithms (EDAs). EDAs guide search by building and sampling an explicit probabilistic model of high-quality solutions and can solve hard problems scalably. However, for very large problems with complex evaluations, their performance may not be sufficient. The authors from the University of Missouri propose methods for improving the efficiency of EDAs.

Analysis of Evolutionary Algorithms on the One-Dimensional Spin Glass with Po...Martin Pelikan

The document analyzes evolutionary algorithms for solving instances of the one-dimensional spin glass problem with power-law interactions. It generates 610,000 problem instances with varying system size (n=20-150) and power-law decay parameter (σ=0-2). It compares the performance of genetic algorithms, hierarchical Bayesian optimization, and local search methods on these instances, measuring the number of evaluations required to find the optimal solution. The results show how performance depends on the power-law decay and effective interaction range.

iBOA: The Incremental Bayesian Optimization AlgorithmMartin Pelikan

This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.

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Fitness inheritance in the Bayesian optimization algorithmMartin Pelikan

This document discusses the enhancement of the Bayesian Optimization Algorithm (BOA) by incorporating fitness inheritance to improve evaluation efficiency in complex real-world problems. It outlines the methodology for extending Bayesian networks to include fitness information and presents experimental results demonstrating significant speed-ups in solution evaluation. The findings indicate that high proportions of inherited fitness information lead to optimal performance improvements, particularly in challenging optimization scenarios.

Using Problem-Specific Knowledge and Learning from Experience in Estimation o...Martin Pelikan

The document discusses the use of prior problem-specific knowledge and learning from experience in estimation of distribution algorithms (EDAs) to enhance their efficiency. It outlines methods for biasing solutions and models based on historical data, which can significantly improve the performance of EDAs. The paper emphasizes that while EDAs can solve problems effectively, there is substantial room for improvement by leveraging previous experiences and expert knowledge.

Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOAMartin Pelikan

This document discusses the application of hierarchical Bayesian optimization algorithms (HBOA) and their relationship with additively decomposable functions (ADFs) in model-directed optimization techniques. It highlights the importance of instance-based bias and distance metrics in solving complex optimization problems efficiently. The authors illustrate their approach and results through examples and comparative analysis of various algorithms and their performance in specific problem landscapes.

Initial-Population Bias in the Univariate Estimation of Distribution AlgorithmMartin Pelikan

This document studies the effects of biasing the initial population in the Univariate Marginal Distribution Algorithm (UMDA) on the onemax and noisy onemax problems. Theoretical models are developed to predict the impact on population size, number of generations, and number of evaluations for different levels of initial bias. Experiments match the theoretical predictions, showing that a positively biased initial population improves performance while a negatively biased population harms performance. Introducing noise does not change these effects.

Analyzing Probabilistic Models in Hierarchical BOA on Traps and Spin GlassesMartin Pelikan

The document discusses the analysis of hierarchical Bayesian optimization algorithms (hboa) in solving complex problems like traps and spin glasses. It explores the efficiency and scalability of hboa models, detailing experiments with trap-5 and 2D Ising spin glasses to understand necessary and unnecessary dependencies in problem sets. The findings indicate that while hboa can create accurate models, further research is needed to develop automated techniques that leverage these models for efficiency enhancement in various challenging problems.

The Bayesian Optimization Algorithm with Substructural Local SearchMartin Pelikan

The document discusses the Bayesian Optimization Algorithm (BOA) enhanced with substructural local search, focusing on improving efficiency through probabilistic modeling and surrogate fitness learning. It explores the incorporation of substructural neighborhoods to optimize solution exploration while maintaining generality. Key experimental results indicate significant speedups and the importance of accurate model structures in achieving effective local search strategies.

Order Or Not: Does Parallelization of Model Building in hBOA Affect Its Scala...Martin Pelikan

1. Martin Pelikan and James D. Laury Jr. study the effects of parallelizing the model building process in hBOA on its scalability. 2. They find that parallelizing model building provides nearly linear speedups but can slightly increase the number of function evaluations needed. 3. However, the negative effects of parallelizing model building are negligible compared to the performance gains, so parallelizing model building is beneficial when it is the bottleneck.

Estimation of Distribution Algorithms TutorialMartin Pelikan

The document discusses probabilistic model-building genetic algorithms (PMBGAs), emphasizing their application in genetic and evolutionary computation for black-box optimization problems. It highlights the shift from traditional methods that rely on variation operators to PMBGAs that utilize machine learning techniques to generate new solutions based on probabilistic models. The talk outlines various representations, advantages, and different algorithm variants like estimation of distribution algorithms (EDAs) and Bayesian optimization algorithms (BOA), pointing to the potential for improved performance in optimization tasks.

Let's get ready to rumble redux: Crossover versus mutation head to head on ex...kknsastry

This document explores the debate between crossover and mutation in genetic algorithms, particularly their performance on exponentially scaled problems. It discusses the conditions under which each operator excels and emphasizes the importance of neighborhood information for effective algorithm performance. The analysis includes empirical studies, scalability comparisons, and conclusions on the efficiency of mutation over crossover in noise-dominated scenarios.

Linkage Learning for Pittsburgh LCS: Making Problems TractableXavier Llorà

The document discusses the development of the Extended Compact Classifier System (ECCS) for improving the linkage learning capabilities in Pittsburgh Learning Classifiers Systems (LCS). It details techniques to evolve rule sets that balance accuracy and generality, and demonstrates the scalability of ECCS through experiments with multiplexer problems, achieving solutions for complex cases. The findings highlight the importance of linkage learning in effectively solving larger multiplexer problems using Pittsburgh LCS methodologies.

Tutorial rpomosi2005

HIS'2008: Genetic-based Synthetic Data Sets for the Analysis of Classifiers B...Albert Orriols-Puig

This document presents a genetic algorithm approach to generating synthetic data sets for analyzing classifier behavior. The genetic algorithm represents data set labelings as binary strings and uses genetic operators like crossover and mutation to evolve solutions that satisfy the desired complexity based on class boundary length. Experiments show the genetic algorithm can generate intermediate complexity data sets in early generations and produce similar accuracy rates across different classifier paradigms, while allowing control over the data set properties. Future work aims to improve efficiency and scalability, enable multiple criteria optimization, and develop benchmark problems with more realistic structure.

Genetic AlgorithmsKarthik Sankar

This document provides an introduction to genetic algorithms. It explains that genetic algorithms are inspired by Darwinian evolution and use processes like selection, crossover and mutation to iteratively improve a population of potential solutions. It discusses how genetic algorithms can be used for optimization problems and classification in data mining. Examples of genetic algorithm applications like the traveling salesman problem are also presented to illustrate genetic algorithm concepts and processes.

Introducing LCS to Digital Design VerificationDaniele Loiacono

The document introduces the use of learning classifier systems (LCS) for digital design verification. It defines what a design and design verification are, and explains that design verification aims to discover bugs before manufacturing by maximizing test coverage. The document notes that design verification is challenging due to increasing design complexity and limited resources. It summarizes previous attempts using genetic algorithms, genetic programming, Bayesian networks and Markov models that achieved good results but had limitations. The document proposes using an LCS called XCS to overcome these limitations through adaptive learning and developing an accurate problem representation.

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