Building Block Identification Research Articles

Specific Non-Reducing Ends in Heparins from Different Animal Origins: Building Blocks Analysis Using Reductive Amination Tagging by Sulfanilic Acid.

Heparins are linear sulfated polysaccharides widely used as anticoagulant drugs. Their nonreducing-end (NRE) has been little investigated due to challenges in their characterization, but is known to be partly generated by enzymatic cleavage with heparanases, resulting in N-sulfated glucosamines at the NRE. Uronic NRE (specifically glucuronic acids) have been isolated from porcine heparin, with GlcA-GlcNS,3S,6S identified as a porcine-specific NRE marker. To further characterize NRE in heparinoids, a building block analysis involving exhaustive heparinase digestion and subsequent reductive amination with sulfanilic acid was performed. This study describes a new method for identifying heparin classical building blocks and novel NRE building blocks using strong anion exchange chromatography on AS11 columns for the assay, and ion-pair liquid chromatography-mass spectrometry for building block identification. Porcine, ovine, and bovine intestine heparins were analyzed. Generally, NRE on these three heparins are highly sulfated moieties, particularly with 3-O sulfates, and the observed composition of the NRE is highly dependent on heparin origin. At the highest level of specificity, the isolated marker was only detected in porcine heparin. However, the proportion of glucosamines in the NRE and the proportion of glucuronic/iduronic configurations in the NRE uronic moieties greatly varied between heparin types.

Evolutionary Algorithms in Stabilization of Inverted Pendulum

A novel optimization algorithm of fuzzy logic controller (FLC) using genetic algorithms is used to characterize the major design parameters of an FLC known as characteristic parameters. The characteristic parameters simplify the design of FLC which are encoded into a chromosome as an integer string. These are optimized by maximizing the evaluated fitness through genetic operations to achieve the optimized FLC .An effective Genetic Algorithm (GA) is proposed using linkage learning, or building block identification. The genes arranged to have a fitness enhancement is the essence of linkage learning. A perfect and faster extended GA is suggested using an effective method to learn distributions and then by linking them. Stabilization of Inverted pendulum pole angle is taken as test bench.

Learning feature spaces for regression with genetic programming.

Genetic programming has found recent success as a tool for learning sets of features for regression and classification. Multidimensional genetic programming is a useful variant of genetic programming for this task because it represents candidate solutions as sets of programs. These sets of programs expose additional information that can be exploited for building block identification. In this work, we discuss this architecture and others in terms of their propensity for allowing heuristic search to utilize information during the evolutionary process. We investigate methods for biasing the components of programs that are promoted in order to guide search towards useful and complementary feature spaces. We study two main approaches: 1) the introduction of new objectives and 2) the use of specialized semantic variation operators. We find that a semantic crossover operator based on stagewise regression leads to significant improvements on a set of regression problems. The inclusion of semantic crossover produces state-of-the-art results in a large benchmark study of open-source regression problems in comparison to several state-of-the-art machine learning approaches and other genetic programming frameworks. Finally, we look at the collinearity and complexity of the data representations produced by different methods, in order to assess whether relevant, concise, and independent factors of variation can be produced in application.

Semantic variation operators for multidimensional genetic programming.

Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification. Towards this goal, we investigate the use of machine learning as a way to bias which components of programs are promoted, and propose two semantic operators to choose where useful building blocks are placed during crossover. A forward stagewise crossover operator we propose leads to significant improvements on a set of regression problems, and produces state-of-the-art results in a large benchmark study. We discuss this architecture and others in terms of their propensity for allowing heuristic search to utilize information during the evolutionary process. Finally, we look at the collinearity and complexity of the data representations that result from these architectures, with a view towards disentangling factors of variation in application.

The use of explicit building blocks in evolutionary computation

This paper proposes a new algorithm to identify and compose building blocks. Building blocks are interpreted as common subsequences between good individuals. The proposed algorithm can extract building blocks from a population explicitly. Explicit building blocks are identified from shared alleles among multiple chromosomes. These building blocks are stored in an archive. They are recombined to generate offspring. The additively decomposable problems and hierarchical decomposable problems are used to validate the algorithm. The results are compared with the Bayesian optimisation algorithm, the hierarchical Bayesian optimisation algorithm, and the chi-square matrix. This proposed algorithm is simple, effective, and fast. The experimental results confirm that building block identification is an important process that guides the recombination procedure to improve the solutions. In addition, the method efficiently solves hard problems.

Mass Customization and Beyond – Application of Co-Creation & Postponement

Rapid technological changes and shortening product life cycles are fundamentally altering the nature of competition in many industries by diffusing competitive advantage. The dominant paradigm, 'mass-production,' is being challenged by the emerging paradigm, 'mass customization.' Mass customization (MC) means production of goods and services for a (relatively) large market which exactly meets the needs of every individual customer with regard to certain product characteristics (differentiation option) at costs roughly corresponding to those of standard mass produced goods. Mass customization enhances profitability through a synergy of increasing customer-perceived value and reducing the costs of production and logistics. Within the knowledge domain of mass customization, this paper focuses on three interrelated strategies: co-creation, modularization and postponement. Also outlined is a road map for building competitive advantage through mass customization based on involvement of customers during early stages of product development and application of postponement by building block identification and product platform development.

Probabilistic building block identification for the optimal design and rehabilitation of water distribution systems

The multi-objective design and rehabilitation of water distribution systems (WDS) is defined as the search for the set of system designs which offers the best trade-off between competing design objectives. Typically these objectives will consist of the cost of implementing a system design and a measure of the performance of that system. These measures are often in competition since improvements in the performance of a system generally come at a cost. Here three genetic algorithms which use probabilistic methods to identify building blocks—the Univariate Marginal Distribution Algorithm (UMDA) (Mühlenbein 1997), the hierarchical Bayesian Optimisation Algorithm (hBOA) (Pelikan 2002) and the Chi-Square Matrix methodology (Aporntewan & Chongstitvatana 2004)—are compared to the well-known multi-objective evolutionary algorithm NSGAII (Deb et al. 2002) for the multi-objective design and rehabilitation of water distribution systems. For single-objective problems the identification of building blocks has been seen to make evolutionary algorithms more scalable to large problems than simple genetic algorithms. In this paper these algorithms are shown to offer significantly better solutions than NSGA-II for the case of large systems. However, this improvement comes at the expense of diversity of solutions in the fronts identified.

Building-block Identification by Simultaneity Matrix

This paper presents a study of building blocks (BBs) in the context of genetic algorithms (GAs). In GAs literature, the BBs are common structures of high-quality solutions. The aim is to identify and maintain the BBs while performing solution recombination. To identify the BBs, we construct an $$\ell \times \ell$$ simultaneity matrix according to a set of $$\ell$$-bit solutions. The matrix element in row i and column j denoted by m ij is the degree of dependency between bit i and bit j. We search for a partition of $${0, \ldots, \ell-1}$$ for the matrix. The main idea of partitioning is to put i and j of which m ij is significantly high in the same partition subset. The partition represents the bit positions of BBs. The partition is exploited in solution recombination so that the bits governed by the same partition subset are passed together. It can be shown that by exploiting the simultaneity matrix the additively decomposable functions can be solved in a polynomial relationship between the number of function evaluations required to reach the optimum and the problem size. A comparison to the Bayesian optimization algorithm (BOA) is made. Empirical results show that the BOA uses less number of function evaluations than that of our algorithm. However, computing the matrix is ten times faster than constructing the Bayesian network.

Towards high value-added products and services: mass customization and beyond

Mass customization enhances profitability through a synergy of increasing customer-perceived value and reducing the costs of production and logistics. It inherently makes high value-added products and services possible through premium profits derived from customized products. This strength is particularly attractive to OEM-based industries. This paper discusses the opportunities and challenges of mass customization for manufacturing industries and service providers. Also outlined is a technological road map for implementing mass customization based on building block identification, product platform development, and product life-cycle integration.