Identification of probabilistic cellular automata

The identification of probabilistic cellular automata (PCA) is studied using a new two stage neighborhood detection algorithm. It is shown that a binary probabilistic cellular automaton (BPCA) can be described by an integer-parameterized polynomial corrupted by noise. Searching for the correct neighborhood of a BPCA is then equivalent to selecting the correct terms which constitute the polynomial model of the BPCA, from a large initial term set. It is proved that the contribution values for the correct terms can be calculated independently of the contribution values for the noise terms. This allows the neighborhood detection technique developed for deterministic rules in to be applied with a larger cutoff value to discard the majority of spurious terms and to produce an initial presearch for the BPCA neighborhood. A multiobjective genetic algorithm (GA) search with integer constraints is then evolved to refine the reduced neighborhood and to identify the polynomial rule which is equivalent to the probabilistic rule with the largest probability. A probability table representing the BPCA can then be determined based on the identified neighborhood and the deterministic rule. The new algorithm is tested over a large set of one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) BPCA rules. Simulation results demonstrate the efficiency of the new method

Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis

Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer’s generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers’ generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising

Input variable selection in time-critical knowledge integration applications: A review, analysis, and recommendation paper

This is the post-print version of the final paper published in Advanced Engineering Informatics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.The purpose of this research is twofold: first, to undertake a thorough appraisal of existing Input Variable Selection (IVS) methods within the context of time-critical and computation resource-limited dimensionality reduction problems; second, to demonstrate improvements to, and the application of, a recently proposed time-critical sensitivity analysis method called EventTracker to an environment science industrial use-case, i.e., sub-surface drilling. Producing time-critical accurate knowledge about the state of a system (effect) under computational and data acquisition (cause) constraints is a major challenge, especially if the knowledge required is critical to the system operation where the safety of operators or integrity of costly equipment is at stake. Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result in a specific state of the system, is the core challenge of this research. The main objective is then to identify which set of input data signals has a significant impact on the set of system state information (i.e. output). Through a cause-effect analysis technique, the proposed technique supports the filtering of unsolicited data that can otherwise clog up the communication and computational capabilities of a standard supervisory control and data acquisition system. The paper analyzes the performance of input variable selection techniques from a series of perspectives. It then expands the categorization and assessment of sensitivity analysis methods in a structured framework that takes into account the relationship between inputs and outputs, the nature of their time series, and the computational effort required. The outcome of this analysis is that established methods have a limited suitability for use by time-critical variable selection applications. By way of a geological drilling monitoring scenario, the suitability of the proposed EventTracker Sensitivity Analysis method for use in high volume and time critical input variable selection problems is demonstrated.E

Methodology for Analyzing and Characterizing Error Generation in Presence of Autocorrelated Demands in Stochastic Inventory Models

Most techniques that describe and solve stochastic inventory problems rely upon the assumption of identically and independently distributed (IID) demands. Stochastic inventory formulations that fail to capture serially-correlated components in the demand lead to serious errors. This dissertation provides a robust method that approximates solutions to the stochastic inventory problem where the control review system is continuous, the demand contains autocorrelated components, and the lost sales case is considered. A simulation optimization technique based on simulated annealing (SA), pattern search (PS), and ranking and selection (R&S) is developed and used to generate near-optimal solutions. The proposed method accounts for the randomness and dependency of the demand as well as for the inherent constraints of the inventory model. The impact of serially-correlated demand is investigated for discrete and continuous dependent input models. For the discrete dependent model, the autocorrelated demand is assumed to behave as a discrete Markov-modulated chain (DMC), while a first-order autoregressive AR(1) process is assumed for describing the continuous demand. The effects of these demand patterns combined with structural cost variations on estimating both total costs and control policy parameters were examined. Results demonstrated that formulations that ignore the serially-correlated component performed worse than those that considered it. In this setting, the effect of holding cost and its interaction with penalty cost become stronger and more significant as the serially-correlated component increases. The growth rate in the error generated in total costs by formulations that ignore dependency components is significant and fits exponential models. To verify the effectiveness of the proposed simulation optimization method for finding the near-optimal inventory policy at different levels of autocorrelation factors, total costs, and stockout rates were estimated. The results provide additional evidence that serially-correlated components in the demand have a relevant impact on determining inventory control policies and estimating measurement of performance

Optimization of Stochastic Discrete Event Simulation Models

Many systems in logistics can be adequately modeled using stochastic discrete event simulation models. Often these models are used to find a good or optimal configuration of the system. This implies that optimization algorithms have to be coupled with the models. Optimization of stochastic simulation models is a challenging research topic since the approaches should be efficient, reliable and should provide some guarantee to find at least in the limiting case with a runtime going to infinite the optimal solution with a probability converging to 1. The talk gives an overview on the state of the art in simulation optimization. It shows that hybrid algorithms combining global and local optimization methods are currently the best class of optimization approaches in the area and it outlines the need for the development of software tools including available algorithms

Multi-agent system for dynamic manufacturing system optimization

This paper deals with the application of multi-agent system concept for optimization of dynamic uncertain process. These problems are known to have a computationally demanding objective function, which could turn to be infeasible when large problems are considered. Therefore, fast approximations to the objective function are required. This paper employs bundle of intelligent systems algorithms tied together in a multi-agent system. In order to demonstrate the system, a metal reheat furnace scheduling problem is adopted for highly demanded optimization problem. The proposed multi-agent approach has been evaluated for different settings of the reheat furnace scheduling problem. Particle Swarm Optimization, Genetic Algorithm with different classic and advanced versions: GA with chromosome differentiation, Age GA, and Sexual GA, and finally a Mimetic GA, which is based on combining the GA as a global optimizer and the PSO as a local optimizer. Experimentation has been performed to validate the multi-agent system on the reheat furnace scheduling problem