Environment identification based memory scheme for estimation of distribution algorithms in dynamic environments

Copyright @ Springer-Verlag 2010.In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented by a probability model. This means that the priority search areas of the solution space are characterized by the probability model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt binary-coded EDAs to solve dynamic optimization problems (DOPs). Within this scheme, the probability models that characterize the search space of the changing environment are stored and retrieved to adapt EDAs according to environmental changes. A diversity loss correction scheme and a boundary correction scheme are combined to counteract the diversity loss during the static evolutionary process of each environment. Experimental results show the validity of the EI-MMS and indicate that the EI-MMS can be applied to any binary-coded EDAs. In comparison with three state-of-the-art algorithms, the univariate marginal distribution algorithm (UMDA) using the EI-MMS performs better when solving three decomposable DOPs. In order to understand the EI-MMS more deeply, the sensitivity analysis of parameters is also carried out in this paper.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant 60774064, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01

Prediction of Formation Conditions of Gas Hydrates Using Machine Learning and Genetic Programming

The formation of gas hydrates in the pipelines of oil, gas, chemical, and other industries has been a significant problem for many years because the formation of gas hydrates may block the pipelines. Hence, the knowledge of the phase equilibrium conditions of gas hydrate became necessary for the economic and safe working of oil, gas, chemical industries. Various thermodynamic approaches with various mathematical techniques are available for the prediction of formation conditions of gas hydrates. In this chapter, the authors have discussed the least square support vector machine and artificial neural network models for the prediction of stability conditions of gas hydrates and the use of genetic programming (GP) and genetic algorithm (GA) to develop a generalized correlation for predicting equilibrium conditions of gas hydrates

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed

Ant colony optimization approach for stacking configurations

In data mining, classifiers are generated to predict the class labels of the instances. An ensemble is a decision making system which applies certain strategies to combine the predictions of different classifiers and generate a collective decision. Previous research has empirically and theoretically demonstrated that an ensemble classifier can be more accurate and stable than its component classifiers in most cases. Stacking is a well-known ensemble which adopts a two-level structure: the base-level classifiers to generate predictions and the meta-level classifier to make collective decisions. A consequential problem is: what learning algorithms should be used to generate the base-level and meta-level classifier in the Stacking configuration? It is not easy to find a suitable configuration for a specific dataset. In some early works, the selection of a meta classifier and its training data are the major concern. Recently, researchers have tried to apply metaheuristic methods to optimize the configuration of the base classifiers and the meta classifier. Ant Colony Optimization (ACO), which is inspired by the foraging behaviors of real ant colonies, is one of the most popular approaches among the metaheuristics. In this work, we propose a novel ACO-Stacking approach that uses ACO to tackle the Stacking configuration problem. This work is the first to apply ACO to the Stacking configuration problem. Different implementations of the ACO-Stacking approach are developed. The first version identifies the appropriate learning algorithms in generating the base-level classifiers while using a specific algorithm to create the meta-level classifier. The second version simultaneously finds the suitable learning algorithms to create the base-level classifiers and the meta-level classifier. Moreover, we study how different kinds on local information of classifiers will affect the classification results. Several pieces of local information collected from the initial phase of ACO-Stacking are considered, such as the precision, f-measure of each classifier and correlative differences of paired classifiers. A series of experiments are performed to compare the ACO-Stacking approach with other ensembles on a number of datasets of different domains and sizes. The experiments show that the new approach can achieve promising results and gain advantages over other ensembles. The correlative differences of the classifiers could be the best local information in this approach. Under the agile ACO-Stacking framework, an application to deal with a direct marketing problem is explored. A real world database from a US-based catalog company, containing more than 100,000 customer marketing records, is used in the experiments. The results indicate that our approach can gain more cumulative response lifts and cumulative profit lifts in the top deciles. In conclusion, it is competitive with some well-known conventional and ensemble data mining methods

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments

Complex and Adaptive Dynamical Systems: A Primer

An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems. It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.Comment: unformatted version of the textbook; published in Springer, Complexity Series (2008, second edition 2010

Tilalämmityksen kysyntäjousto mallipohjaisella algoritmilla toimistorakennuksessa

Decreasing the CO2 emissions of building stock plays a remarkable role in the mitigation of global warming. The share of building sector from both the global final energy use and CO2 emissions is about 30%. Demand response of electricity and district heating provides one tool for decreasing emissions in the whole energy system. In demand response the buildings energy use is controlled so that the peak-load consumption in the energy grid decreases and the consumption profile stabilizes. CO2 emissions are reduced since the need for emission-intensive peak-demand generation decreases. The building owners benefit from the energy cost savings and the energy producers from the higher grid efficiency and decreased investments for peak-demand power plants. The main objective of this thesis was to define the potential of space heating demand response in the perspective of local thermal comfort, cost savings and energy flexibility. Demand response was implemented using a model predictive control algorithm (MPC) that optimized and controlled the space heating temperature setpoints. The MPC algorithm was tested with dynamical simulation model of an educational office building located in Aalto University campus area. The second research question was to examine how the demand response of space heating affects the local thermal comfort of occupants. The draught risk during the demand response was investigated by thermal manikin measurements in workstations near windows. To prevent the draught risk, a window surface temperature restriction was implemented in the MPC control algorithm and its influence on the demand response potential was investigated with different properties of windows. The thermal comfort measurements showed that the draught risk increased in workstations adjacent to windows during the decreased heating power. The increase in draught risk was noticed when the window surface temperature dropped below 15 °C while the heating was turned OFF. The influence from the window surface temperature restriction on the demand response potential was found to be small. With energy efficient windows, the influence was negligible and with non-energy efficient windows the demand response potential was affected only when unnecessary high power requirements were set. Using the MPC algorithm, the annual heating cost of the case building could be decreased 4.7%. The highest energy flexibility obtained was 14%.Rakennusten hiilidioksidipäästöjen vähentämisellä voidaan edistää merkittävästi ilmastonmuutoksen torjumista, sillä rakennusten osuus kokonaisenergiankulutuksesta (ja hiilidioksidipäästöistä) maailmassa on noin 30%. Sähkön ja lämmön kysyntäjousto rakennuksissa on yksi keino koko energiajärjestelmän kasvihuonepäästöjen vähentämiseen. Kysyntäjoustossa kuluttajat muuttavat kulutustaan siten, että energiaverkon huipputehon tarve laskee ja kulutuksesta tulee stabiilimpaa. Kysyntäjousto vähentää kasvihuonepäästöjä, sillä energia- ja päästöintensiivisiä huippuvoimalaitosten käyttötarve vähenee. Kysyntäjoustosta on hyötyä rakennusten omistajille kustannussäästöjen muodossa ja energiayhtiöille investointitarpeen pienenemisenä sekä verkon hyötysuhteen paranemisena. Tämän tutkimuksen tavoitteena oli tutkia tilojen lämmityksen kysyntäjoustopotentiaalia kustannussäästöjen, energiankäytön joustavuuden ja lämpöviihtyvyyden näkökulmasta. Lämmityksen kysyntäjousto toteutettiin tilojen lämmitystä ohjaavan mallipohjaisen algoritmin avulla. Algoritmia testattiin Aalto yliopiston kampusalueella sijaitsevaan opetusrakennukseen dynaamisen simulointityökalun avulla. Toisena tutkimuskysymyksenä oli selvittää millainen vaikutus lämmityksen kysyntäjoustolla on lokaaliin lämpöviihtyvyyteen. Tässä työssä kysyntäjouston vaikutusta vetoriskiin tutkittiin kokeellisesti lämpönuken avulla työpisteissä, jotka sijaitsivat ikkunoiden lähellä. Kylmistä ikkunapinnoista johtuvan vetoriskin välttämiseksi kysyntäjoustolle asetettiin rajoite mallipohjaisessa algoritmissa, jonka vaikutusta kysyntäjoustopotentiaaliin tutkittiin erilaisilla ikkunoiden ominaisuuksilla. Kokeelliset lämpöviihtyvyysmittaukset osoittivat, että vetoriski ikkunoiden lähellä sijaitsevissa toimistopisteissä kasvaa, kun pattereiden tehoa lasketaan kysyntäjouston aikana. Vetoriskin huomattiin kasvavan, mikäli ikkunan pintalämpötila laski alle 15 °C, kun patterit eivät olleet päällä. Vetoriskin pienentämiseksi tehdyn rajoitteen vaikutus kysyntäjoustolla saavutettaviin kustannussäästöihin sekä energiajoustavuuteen huomattiin olevan pieni. Energiatehokkailla ikkunoilla vaikutus kysyntäjoustopotentiaaliin oli mitätön, ja huonoilla (U-arvo = 2,6 W/m2K) ikkunoilla potentiaali laski vasta tarpeettoman suurilla lämmitystehon korotuksilla. Mallipohjaisen algoritmin avulla tutkitun toimistorakennuksen vuotuisia lämmityskustannuksia pystyttiin vähentämään noin 4.7%. lämmityksen joustavuudeksi saatiin parhaassa tapauksessa 14%

Uniting In Silico and In Vivo Systems Biology: a New Concept to Approximate Theory to Real-Life Flux Distributions

Within the last years, bioinformatics expanded from its focus on protein related research to the investigation of whole organisms. Up to date, a variety of bacteria has been modeled, in most detail Escherichia coli. As a systemic approach, flux balance analysis (FBA) has established itself in the scientific community to analyze steady state flux distributions. Within FBA the metabolic network is expressed in terms of a matrix, called the stoichiometric matrix. The assumption of the system to exist in a (temporary) steady state leads to a homogeneous linear system of equations, which is typically underdetermined. By application of an objective function and computation of the linear program that unfolds, one can select one discrete solution out of the existing solution space. In this work, we built a genome based model of the Corynebacterium glutamicum and analyzed it in terms of flux balance analysis. We implemented an enhancement of FBA, called energy balance analysis, that considers thermodynamical issues. We further used metabolic profiling data to impose more constraints on the analyses. By comparing the organism under different environmental conditions, we were able to neglect unknown kinetic constants and to establish new requirements during the energy balance analysis. Namely, we used data derived by raising the C. glutamicum on acetate or glucose. This procedure leads to a further reduction of the solution space and thereby helps to close the gap between predictions and real-life flux distributions. The comprehensive nature of the technique enables it to be applied to any model and to be combined with any other enhancement of the flux balance analysis