Category: Uncategorized

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

SIGEVOlution Volume 4 Issue 2

The new issue of SIGEVOlution is now available for you to download from: http://www.sigevolution.org The issue features: 45 Years of Evolution Strategies: Hans-Paul Schwefel Interviewed for the Genetic Argonaut Blog CIG-2009 Dissertation Corner Calls & calendar The newsletter is intended … Continue reading →

Mount Hood, Oregon

The new issue of SIGEVOlution is now available for you to download from:
http://www.sigevolution.org

The issue features:

  • 45 Years of Evolution Strategies: Hans-Paul Schwefel Interviewed for the Genetic Argonaut Blog
  • CIG-2009
  • Dissertation Corner
  • Calls & calendar

The newsletter is intended to be viewed electronically.

Pier Luca Lanzi (EIC)

GECCO-2010 deadline extended

The submission deadline for the 2010 Genetic and Evolutionary Computation Conference (GECCO-2010) has been extended to January 27, 2010. See the conference web site for more details.

The submission deadline for the 2010 Genetic and Evolutionary Computation Conference (GECCO-2010) has been extended to January 27, 2010. See the conference web site for more details.

A particle swarm optimization based memetic algorithm for dynamic optimization problems

Abstract  Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems
since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimizat…

Abstract  

Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems
since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based
memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework
of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator
and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random
immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks
in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic
algorithm is robust and adaptable in dynamic environments.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9176-2
  • Authors
    • Hongfeng Wang, School of Information Science and Engineering, Northeastern University, Shenyang, 110004 People’s Republic of China
    • Shengxiang Yang, Department of Computer Science, University of Leicester, University Road, Leicester, LE1 7RH UK
    • W. H. Ip, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, People’s Republic of China
    • Dingwei Wang, School of Information Science and Engineering, Northeastern University, Shenyang, 110004 People’s Republic of China

Swarm intelligence: the state of the art special issue of natural computing

Swarm intelligence: the state of the art special issue of natural computing
Content Type Journal ArticleDOI 10.1007/s11047-009-9172-6Authors
Eric Bonabeau, Icosystem, 10 Fawcett Street, Cambridge, MA 02138, USADavid Corne, Heriot-Watt University, Ed…

Swarm intelligence: the state of the art special issue of natural computing

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9172-6
  • Authors
    • Eric Bonabeau, Icosystem, 10 Fawcett Street, Cambridge, MA 02138, USA
    • David Corne, Heriot-Watt University, Edinburgh, EH14 4AS UK
    • Riccardo Poli, School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ UK

Multiobjective particle swarm optimization with nondominated local and global sets

Abstract  In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle
of the population has a great impact on the convergence and diversity of solutions, especially when optim…

Abstract  

In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle
of the population has a great impact on the convergence and diversity of solutions, especially when optimizing problems with
high number of objectives. This paper presents an approach using two sets of nondominated solutions. The ability of the proposed
approach to detect the true Pareto optimal solutions and capture the shape of the Pareto front is evaluated through experiments
on well-known non-trivial multiobjective test problems as well as the real-life electric power dispatch problem. The diversity
of the nondominated solutions obtained is demonstrated through different measures. The proposed approach has been assessed
through a comparative study with the reported results in the literature.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9171-7
  • Authors
    • M. A. Abido, Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261 Saudi Arabia

Special issue on Nature Inspired Cooperative Strategies for Optimisation (NICSO)

Special issue on Nature Inspired Cooperative Strategies for Optimisation (NICSO)
Content Type Journal ArticleDOI 10.1007/s11047-009-9177-1Authors
N. Krasnogor, University of Catania Catania ItalyG. Nicosia, University of Catania Catania ItalyM. Pavo…

Special issue on Nature Inspired Cooperative Strategies for Optimisation (NICSO)

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9177-1
  • Authors
    • N. Krasnogor, University of Catania Catania Italy
    • G. Nicosia, University of Catania Catania Italy
    • M. Pavone, University of Catania Catania Italy
    • D. A. Pelta, University of Catania Catania Italy

Bacteria that turn gears

An interesting inversion of several of the themes covered in this journal:http://news.yahoo.com/s/livescience/20091226/sc_livescience/scientistsharnessbacteriatoturnmicroscopicgearsIt would be even more interesting if gear-turning performance drove sel…

An interesting inversion of several of the themes covered in this journal:
It would be even more interesting if gear-turning performance drove selection…
-Lee

Flocking based approach for data clustering

Abstract  Data clustering is a process of extracting similar groups of the underlying data whose labels are hidden. This paper describes
different approaches for solving data clustering problem. Particle swarm optimization (PSO) has been rec…

Abstract  

Data clustering is a process of extracting similar groups of the underlying data whose labels are hidden. This paper describes
different approaches for solving data clustering problem. Particle swarm optimization (PSO) has been recently used to address
clustering task. An overview of PSO-based clustering approaches is presented in this paper. These approaches mimic the behavior
of biological swarms seeking food located in different places. Best locations for finding food are in dense areas and in regions
far enough from others. PSO-based clustering approaches are evaluated using different data sets. Experimental results indicate
that these approaches outperform K-means, K-harmonic means, and fuzzy c-means clustering algorithms.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9173-5
  • Authors
    • Abbas Ahmadi, Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran
    • Fakhri Karray, Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON, Canada
    • Mohamed S. Kamel, Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON, Canada

A novel particle swarm niching technique based on extensive vector operations

Abstract  Several techniques have been proposed to extend the particle swarm optimization (PSO) paradigm so that multiple optima can
be located and maintained within a convoluted search space. A significant number of these implementations ar…

Abstract  

Several techniques have been proposed to extend the particle swarm optimization (PSO) paradigm so that multiple optima can
be located and maintained within a convoluted search space. A significant number of these implementations are subswarm-based,
that is, portions of the swarm are optimized separately. Niches are formed to contain these subswarms, a process that often
requires user-specified parameters. The proposed technique, known as the vector-based PSO, uses a novel approach to locate
and maintain niches by using additional vector operations to determine niche boundaries. As the standard PSO uses weighted
vector combinations to update particle positions and velocities, the niching technique builds upon existing knowledge of the
particle swarm. Once niche boundaries have been calculated, the swarm can be organized into subswarms without prior knowledge
of the number of niches and their corresponding niche radii. This paper presents the vector-based PSO with emphasis on its
underlying principles. Results for a number of functions with different characteristics are reported and discussed. The performance
of the vector-based PSO is also compared to two other niching techniques for particle swarm optimization.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9170-8
  • Authors
    • I. L. Schoeman, Department of Computer Science, University of Pretoria, Lynnwood Road, Pretoria, South Africa
    • A. P. Engelbrecht, Department of Computer Science, University of Pretoria, Lynnwood Road, Pretoria, South Africa