Solution of fuzzy polynomial equations by modified Adomian decomposition method

Abstract  In this paper, we present some efficient numerical algorithm for solving fuzzy polynomial equations based on Newton’s method.
The modified Adomian decomposition method is applied to construct the numerical algorithms. Some numeri…

Abstract  In this paper, we present some efficient numerical algorithm for solving fuzzy polynomial equations based on Newton’s method.
The modified Adomian decomposition method is applied to construct the numerical algorithms. Some numerical illustrations are
given to show the efficiency of algorithms.

  • Content Type Journal Article
  • Pages 1-6
  • DOI 10.1007/s00500-010-0546-6
  • Authors
    • M. Otadi, Islamic Azad University Department of Mathematics Firuozkooh Branch Firuozkooh Iran
    • M. Mosleh, Islamic Azad University Department of Mathematics Firuozkooh Branch Firuozkooh Iran

Extremal states on bounded residuated -monoids with general comparability

Abstract  Bounded residuated lattice ordered monoids (

Rl
-monoids) are a common generalization of pseudo-

BL
-algebras and Heyting algebras, i.e. algebras of the non-commutative basic fuzzy logic (and consequently of the basic fuzzy
logic…

Abstract  Bounded residuated lattice ordered monoids (

Rl

-monoids) are a common generalization of pseudo-

BL

-algebras and Heyting algebras, i.e. algebras of the non-commutative basic fuzzy logic (and consequently of the basic fuzzy
logic, the Łukasiewicz logic and the non-commutative Łukasiewicz logic) and the intuitionistic logic, respectively. We investigate
bounded

Rl

-monoids satisfying the general comparability condition in connection with their states (analogues of probability measures).
It is shown that if an extremal state on Boolean elements fulfils a simple condition, then it can be uniquely extended to
an extremal state on the

Rl

-monoid, and that if every extremal state satisfies this condition, then the

Rl

-monoid is a pseudo-

BL

-algebra.

  • Content Type Journal Article
  • Pages 1-5
  • DOI 10.1007/s00500-010-0545-7
  • Authors
    • Jiří Rachůnek, Palacký University Department of Algebra and Geometry, Faculty of Sciences Tomkova 40 779 00 Olomouc Czech Republic
    • Dana Šalounová, VŠB-Technical University Ostrava Sokolská 33 701 21 Ostrava Czech Republic

Evolving robust GP solutions for hedge fund stock selection in emerging markets

Abstract  Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment
in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be i…

Abstract  Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment
in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be improved so that solutions
are produced that are robust to non-trivial changes in the environment? We explore two new approaches. The first approach uses subsets of extreme environments
during training and the second approach uses a voting committee of GP individuals with differing phenotypic behaviour.

  • Content Type Journal Article
  • Pages 1-14
  • DOI 10.1007/s00500-009-0511-4
  • Authors
    • Wei Yan, University College London Department of Computer Science Gower Street London WC1E 6BT UK
    • Christopher D. Clack, University College London Financial Computing, Department of Computer Science Gower Street London WC1E 6BT UK

GPEM 11(1) now available online

The first issue of volume 11 of Genetic Programming and Evolvable Machines is now available online, with the following articles:”Editorial Introduction” and “Acknowledgments” by Lee Spector”The influence of mutation on population dynamics in multiobjec…

The first issue of volume 11 of Genetic Programming and Evolvable Machines is now available online, with the following articles:

“Editorial Introduction” and “Acknowledgments” by Lee Spector
“The influence of mutation on population dynamics in multiobjective genetic programming”
by Khaled Badran & Peter I. Rockett
“Automated synthesis of resilient and tamper-evident analog circuits without a single point of failure”
by Vyung-Joong Kim, Adrian Wong & Hod Lipson
“GP challenge: evolving energy function for protein structure prediction” by Pawel Widera, Jonathan M. Garibaldi & Natalio Krasnogor
“The identification and exploitation of dormancy in genetic programming” by David Jackson
“Book Review: Michael Affenzeller, Stefan Wagner, Stephan Winkler and Andreas Beham: Genetic algorithms and genetic programming modern concepts and practical applications” by Gisele L. Pappa
“Book Review: Melanie Mitchell: Complexity a guided tour”
by Felix Streichert

Gene expression studies with DGL global optimization for the molecular classification of cancer

Abstract  This paper combines a powerful algorithm, called Dongguang Li (DGL) global optimization, with the methods of cancer diagnosis
through gene selection and microarray analysis. A generic approach to cancer classification based on gene…

Abstract  This paper combines a powerful algorithm, called Dongguang Li (DGL) global optimization, with the methods of cancer diagnosis
through gene selection and microarray analysis. A generic approach to cancer classification based on gene expression monitoring
by DNA microarrays is proposed and applied to two test cancer cases, colon and leukemia. The study attempts to analyze multiple
sets of genes simultaneously, for an overall global solution to the gene’s joint discriminative ability in assigning tumors
to known classes. With the workable concepts and methodologies described here an accurate classification of the type and seriousness
of cancer can be made. Using the orthogonal arrays for sampling and a search space reduction process, a computer program has
been written that can operate on a personal laptop computer. Both the colon cancer and the leukemia microarray data can be
classified 100% correctly without previous knowledge of their classes. The classification processes are automated after the
gene expression data being inputted. Instead of examining a single gene at a time, the DGL method can find the global optimum
solutions and construct a multi-subsets pyramidal hierarchy class predictor containing up to 23 gene subsets based on a given
microarray gene expression data collection within a period of several hours. An automatically derived class predictor makes
the reliable cancer classification and accurate tumor diagnosis in clinical practice possible.

  • Content Type Journal Article
  • Pages 1-19
  • DOI 10.1007/s00500-010-0542-x
  • Authors
    • Dongguang Li, Edith Cowan University School of Computer and Security Science 2 Bradford Street Mount Lawley WA 6050 Australia

On stoichiometry for the assembly of flexible tile DNA complexes

Abstract  Given a set of flexible branched junction DNA molecules with sticky-ends (building blocks), called here “tiles”, we consider
the problem of determining the proper stoichiometry such that all sticky-ends could end up connected. …

Abstract  

Given a set of flexible branched junction DNA molecules with sticky-ends (building blocks), called here “tiles”, we consider
the problem of determining the proper stoichiometry such that all sticky-ends could end up connected. In general, the stoichiometry
is not uniform, and the goal is to determine the proper proportion (spectrum) of each type of molecule within a test tube
to allow for complete assembly. According to possible components that assemble in complete complexes we partition multisets
of tiles, called here “pots”, into classes: unsatisfiable, weakly satisfiable, satisfiable and strongly satisfiable. This
classification is characterized through the spectrum of the pot, and it can be computed in PTIME using the standard Gauss-Jordan
elimination method. We also give a geometric description of the spectrum as a convex hull within the unit cube.

  • Content Type Journal Article
  • Pages 1121-1141
  • DOI 10.1007/s11047-009-9169-1
  • Authors
    • N. Jonoska, Department of Mathematics & Statistics, University of South Florida, Tampa, FL 33620, USA
    • G. L. McColm, Department of Mathematics & Statistics, University of South Florida, Tampa, FL 33620, USA
    • A. Staninska, Institute of Biomathematics and Biometry Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

Fast REST API prototyping with Crochet and Scala

I just finished committing the last changes to Crochet and tagged version 0.1.4vcli now publicly available on GitHub (http://github.com/xllora/Crochet). Also feel free to visit the issues page in case you run into question/problems/bugs. Motivation Crochet is a light weight web framework oriented to rapid prototyping of REST APIs. If you are looking for a Rails […]

Related posts:

  1. Meandre 2.0 Alpha Preview = Scala + MongoDB
  2. Meandre is going Scala
  3. Fast mutation implementation for genetic algorithms in Python

I just finished committing the last changes to Crochet and tagged version 0.1.4vcli now publicly available on GitHub (http://github.com/xllora/Crochet). Also feel free to visit the issues page in case you run into question/problems/bugs.

Motivation

Crochet is a light weight web framework oriented to rapid prototyping of REST APIs. If you are looking for a Rails like framework written in Scala, please take a look at Lift at http://liftweb.net/ instead.

Crochet targets quick prototyping of REST APIs relying on the flexibility of the Scala language. The initial ideas for Crochet were inspired while reading Gabriele Renzi post on creating the STEP picoframework with Scala and the need for quickly prototyping APIs for pilot projects. Crochet also provides mechanisms to hide repetitive tasks involved with default responses and authentication/authorization piggybacking on the mechanics provided by application servers.

Who uses Crochet?

Crochet was born from the need for quickly prototyping REST APIs which required exposing legacy code written in Java. I have been actively using Crochet to provide REST APIs for a variety of projects developed at the National Center for Supercomputing Applications. One of the primary adopters and movers of Crochet is the Meandre Infrastructure for data-intensive computing developed under the SEASR project.

Crochet in 2 minuts

Before you start please check you have Scala installed on your system. You can find more information on how to get Scala up and running here.

  1. Get the latest Crochet jar from the Downloads section at GitHub and the third party dependencies.
  2. Copy the following code into a file named hello-world.scala.
    import crochet._
    new Crochet {
         get("/message") { 
             <html>
                   <head><title>Hello World</title></head>
                   <body><h1>Hello World!</h1></body>
             </html>
         }
    } on 8080
  3. Get your server up and running by running (please change the version number if needed)
    $ scala -cp crochet-0.1.4.jar:crochet-3dparty-libraries-0.1.X.jar hello-world.scala
  4. You just have your first _Crochet_ API up and running. You can check the API working by opening your browser and pointing it to http://localhost:8080/message and you should get the message Hello World! back.

    Where to go from here?

    You will find more information on the Crochet wiki at GitHub. The wiki contains basic information as a QuickStart guide (which also includes how to deal with static content), descriptions of the basic concepts used in Crochet, and several examples that can get up and running fast.

    Related posts:

    1. Meandre 2.0 Alpha Preview = Scala + MongoDB
    2. Meandre is going Scala
    3. Fast mutation implementation for genetic algorithms in Python

Simdist: a distribution system for easy parallelization of evolutionary computation

Abstract  This article introduces Simdist, a software tool for parallel execution of evolutionary algorithms (EAs) in a master-slave configuration on cluster architectures.
Clusters have become a cost-effective parallel solution, and the pot…

Abstract  

This article introduces Simdist, a software tool for parallel execution of evolutionary algorithms (EAs) in a master-slave configuration on cluster architectures.
Clusters have become a cost-effective parallel solution, and the potential computational capabilities are phenomenal. However,
the transition from traditional R&D on a personal computer to parallel development and deployment can be a major step. Simdist
simplifies this transition considerably, by separating the task of distributing data across the cluster network from the actual
EA-related processing performed on the master and slave nodes. Simdist is constructed in the vein of traditional Unix command
line tools; it runs in a separate process and communicates with EA child processes via standard input and output. As a result,
Simdist is oblivious to the programming language(s) used in the EA, and the EA is similarly oblivious to the internals of
Simdist.

  • Content Type Journal Article
  • Pages 185-203
  • DOI 10.1007/s10710-009-9100-7
  • Authors
    • Boye Annfelt Høverstad, Norwegian University of Science and Technology (NTNU) Department of Computer and Information Science Trondheim Norway

Melanie Mitchell: Complexity a guided tour

Melanie Mitchell: Complexity a guided tour
Content Type Journal ArticlePages 127-128DOI 10.1007/s10710-009-9097-yAuthors
Felix Streichert, Eberhard Karls Universität Tübingen Wilhelm-Schickard-Institut für Informatik Tübingen Germany

Jour…

Melanie Mitchell: Complexity a guided tour

  • Content Type Journal Article
  • Pages 127-128
  • DOI 10.1007/s10710-009-9097-y
  • Authors
    • Felix Streichert, Eberhard Karls Universität Tübingen Wilhelm-Schickard-Institut für Informatik Tübingen Germany