Author: Community

Adaptive ε-Ranking on many-objective problems

Abstract  This work proposes Adaptive ε-Ranking to enhance Pareto based selection, aiming to develop effective many-objective evolutionary optimization algorithms. ε-Ranking fine grains ranking of solutions after they have been ranked by
P…

Abstract  This work proposes Adaptive ε-Ranking to enhance Pareto based selection, aiming to develop effective many-objective evolutionary optimization algorithms. ε-Ranking fine grains ranking of solutions after they have been ranked by
Pareto dominance, using a randomized sampling procedure combined with ε-dominance to favor a good distribution of the samples.
In the proposed method, sampled solutions keep their initial rank and solutions located within the virtually expanded ε-dominance
regions of the sampled solutions are demoted to an inferior rank. The parameter ε that determines the expanded regions of
dominance of the sampled solutions is adapted at each generation so that the number of best-ranked solutions is kept close
to a desired number that is expressed as a fraction of the population size. We enhance NSGA-II with the proposed method and
analyze its performance on MNK-Landscapes, showing that the adaptive method works effectively and that compared to NSGA-II
convergence and diversity of solutions can be improved remarkably on MNK-Landscapes with 3 ≤ M ≤ 10 objectives. Also, we compare the performance of Adaptive ε-Ranking with two representative many-objective evolutionary
algorithms on DTLZ continuous functions. Results on DTLZ functions with 3 ≤ M ≤ 10 objectives suggest that the three many-objective approaches emphasize different areas of objective space and could be
used as complementary strategies to produce a better approximation of the Pareto front.

  • Content Type Journal Article
  • DOI 10.1007/s12065-009-0031-2
  • Authors
    • Hernán Aguirre, Shinshu University International Young Researcher Empowerment Center, Faculty of Engineering 4-17-1 Wakasato Nagano 380-8553 Japan
    • Kiyoshi Tanaka, Shinshu University Faculty of Engineering 4-17-1 Wakasato Nagano 380-8553 Japan

Special issue on simulated evolution and learning

Special issue on simulated evolution and learning
Content Type Journal ArticleDOI 10.1007/s12065-009-0033-0Authors
Michael Kirley, The University of Melbourne Department of Computer Science and Software Engineering Melbourne AustraliaMengjie Zhang, …

Special issue on simulated evolution and learning

  • Content Type Journal Article
  • DOI 10.1007/s12065-009-0033-0
  • Authors
    • Michael Kirley, The University of Melbourne Department of Computer Science and Software Engineering Melbourne Australia
    • Mengjie Zhang, Victoria University School of Engineering and Computer Science Wellington New Zealand
    • Xiaodong Li, RMIT University School of Computer Science and Information Technology Melbourne Australia

Optical solution for hard on average #P-complete instances (using exponential space for solving instances of the permanent)

Abstract  Optical architectures that use exponential space for solving instances of the (non-necessarily-binary) permanent are presented.
This is the first work to specifically focus on such hard on average problems. Two architectures are su…

Abstract  

Optical architectures that use exponential space for solving instances of the (non-necessarily-binary) permanent are presented.
This is the first work to specifically focus on such hard on average problems. Two architectures are suggested the first is
based on programmable masks, and the second on preprepared fixed number of masks.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9168-2
  • Authors
    • Amir Anter, Department of Computer Science, Ben-Gurion University of the Negev, Beer Sheva, Israel
    • Shlomi Dolev, Department of Computer Science, Ben-Gurion University of the Negev, Beer Sheva, Israel

SIGEVOlution Volume 4, Issue 1, is now available

The new issue of the SIGEVOlution newsletter, Volume 4 Issue 1, is now available for you to download from: http://www.sigevolution.orgThe new issue features:”Computational Intelligence Marketing” by Arthur Kordon”Pyevolve: a Python Open-Source Framewor…

The new issue of the SIGEVOlution newsletter, Volume 4 Issue 1, is now available for you to download from: http://www.sigevolution.org
The new issue features:
  • “Computational Intelligence Marketing” by Arthur Kordon
  • “Pyevolve: a Python Open-Source Framework for Genetic Algorithms” by Christian S. Perone
  • Calls & calendar
The newsletter is intended to be viewed electronically.
Thanks to Pier Luca Lanzi, SIGEvolution Editor-in-Chief.

SIGEVOlution Volume 4, Issue 1

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

The issue features:

Computational Intelligence Marketing by Arthur Kordon

Pyevolve: a Python Open-Source Framework for Genetic Algorithms by Christian S. Perone

Calls & calendar

The newsletter is intended to be viewed electronically.
Pier Luca Lanzi (EIC)
Related Posts

The new issue of SIGEVOlution is now available for you to download from:

http://www.sigevolution.org

The issue features:

  • Computational Intelligence Marketing by Arthur Kordon
  • Pyevolve: a Python Open-Source Framework for Genetic Algorithms by Christian S. Perone
  • Calls & calendar

The newsletter is intended to be viewed electronically.

Pier Luca Lanzi (EIC)

Related Posts

    GPEM 10(4) now available online

    The fourth issue of volume 10 of Genetic Programming and Evolvable Machines is now available online. This is the first part of the two-part Special Issue on Parallel and Distributed Evolutionary Algorithms, and it contains the following articles:Introd…

    The fourth issue of volume 10 of Genetic Programming and Evolvable Machines is now available online. This is the first part of the two-part Special Issue on Parallel and Distributed Evolutionary Algorithms, and it contains the following articles:

    Introduction: special issue on parallel and distributed evolutionary algorithms, part I
    by Marco Tomassini & Leonardo Vanneschi
    Distributed differential evolution with explorative–exploitative population families
    by Matthieu Weber, Ferrante Neri & Ville Tirronen
    A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization
    by V. G. Asouti, I. C. Kampolis & K. C. Giannakoglou
    Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework
    by Asim Munawar, Mohamed Wahib, Masaharu Munetomo & Kiyoshi Akama
    Parallel evolution using multi-chromosome cartesian genetic programming
    by James Alfred Walker, Katharina Völk, Stephen L. Smith & Julian Francis Miller
    Genetic programming on graphics processing units
    by Denis Robilliard, Virginie Marion-Poty & Cyril Fonlupt
    Book Review: Natalio Krasnogor, Steve Gustafson, David A. Pelta, and Jose L. Verdegay (eds): Systems self-assembly: multidisciplinary snapshots
    by Navneet Bhalla

    The effects of time-varying rewards on the evolution of cooperation

    Abstract  Understanding how cooperative behavior emerges within a population of autonomous individuals has been the focus of a great
    deal of research in biology, economics and more recently in the multi-agent systems domain. However, there a…

    Abstract  Understanding how cooperative behavior emerges within a population of autonomous individuals has been the focus of a great
    deal of research in biology, economics and more recently in the multi-agent systems domain. However, there are still many
    open questions. In this paper, we address some of these questions by investigating the effects of time-varying, non-symmetric
    rewards on the evolution of cooperation in the spatial Prisoner’s dilemma game. The rationale behind this approach is based
    on the notion that the associated payoffs from pursuing certain strategies do vary among members of real-world populations.
    In our model, agents with limited cognitive capacity play the game with their local neighbours. In addition to its game playing
    strategy, each agent has additional attributes that can be used to control the number of rounds of the game the agent actually
    participates in, as well as the magnitude of any rewards that it receives. Numerical simulations show that dynamic updates
    to payoff values induce a change in equilibrium cooperation levels. This suggests that heterogeneous payoff values and social
    diversity within a cost-benefit context are important factors in the promotion of cooperation in the spatial Prisoner’s dilemma
    game.

    • Content Type Journal Article
    • DOI 10.1007/s12065-009-0032-1
    • Authors
      • Golriz Rezaei, The University of Melbourne Department of Computer Science and Software Engineering Melbourne VIC Australia
      • Michael Kirley, The University of Melbourne Department of Computer Science and Software Engineering Melbourne VIC Australia

    Binary to modified trinary number system conversion and vice-versa for optical super computing

    Abstract  With the demand of the super fast processing and handling of huge volume of data the scientific workers in the field of computer
    and optics felt the importance of optical computation with multivalued logic. One of the most importan…

    Abstract  

    With the demand of the super fast processing and handling of huge volume of data the scientific workers in the field of computer
    and optics felt the importance of optical computation with multivalued logic. One of the most important number system suitable
    for optical computation with multivalued logic is the modified trinary number (MTN) system because of its carry and borrow-free
    operations. At this juncture to avail the advantages of both the Binary and MTN system the conversion from one system to another
    is most important. In this paper we have communicated the conversion from Binary to MTN and vice-versa including the mixed
    MTN with details of optoelectronic circuit implementation.

    • Content Type Journal Article
    • DOI 10.1007/s11047-009-9166-4
    • Authors
      • Amal K. Ghosh, Department of Applied Electronics and Instrumentation Engineering, Netaji Subhash Engineering College, Techno City, Garia, Kolkata, 700 152 India
      • Amitabha Basuray, Department of Applied Optics and Photonics, University of Calcutta, 92, A.P.C.Road, Kolkata, 700 009 India

    Hybrid Petri net based modeling for biological pathway simulation

    Abstract  Hybrid Petri net (HPN) is an extension of the Petri net formalism, which enables us to handle continuous information in addition
    to discrete information. Firstly, this paper demonstrates how biological pathways can be modeled by th…

    Abstract  

    Hybrid Petri net (HPN) is an extension of the Petri net formalism, which enables us to handle continuous information in addition
    to discrete information. Firstly, this paper demonstrates how biological pathways can be modeled by the integration of discrete
    and continuous elements, with an example of the λ phage genetic switch system including induction and retroregulation mechanisms.
    Although HPN allows intuitive modeling of biological pathways, some fundamental biological processes such as complex formation
    cannot be represented with HPN. Thus, this paper next provides the formal definition of hybrid functional Petri net with extension
    (HFPNe), which has high potential for modeling various kinds of biological processes. Cell Illustrator is a software tool
    developed on the basis of the definition of HFPNe. Hypothesis creation by Cell Illustrator is demonstrated with the example
    of the cyanobacterial circadian gene clock system. Finally, our ongoing tasks, which include the development of a computational
    platform for systems biology, are presented.

    • Content Type Journal Article
    • Pages 1099-1120
    • DOI 10.1007/s11047-009-9164-6
    • Authors
      • Hiroshi Matsuno, Graduate School of Science and Engineering, Yamaguchi University, 1677-1, Yoshida, Yamaguchi 753-8512, Japan
      • Masao Nagasaki, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639 Japan
      • Satoru Miyano, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639 Japan

    Numerical simplification for bloat control and analysis of building blocks in genetic programming

    Abstract  In tree-based genetic programming, there is a tendency for the size of the programs to increase from generation to generation,
    a phenomenon known as bloat. It is standard practice to place some form of control on program size eithe…

    Abstract  In tree-based genetic programming, there is a tendency for the size of the programs to increase from generation to generation,
    a phenomenon known as bloat. It is standard practice to place some form of control on program size either by limiting the
    number of nodes or the depth of the program trees, or by adding a component to the fitness function that rewards smaller programs
    (parsimony pressure). Others have proposed directly simplifying individual programs using algebraic methods. In this paper,
    we add node-based numerical simplification as a tree pruning criterion to control program size. We investigate the effect
    of on-line program simplification, both algebraic and numerical, on program size and resource usage. We also investigate the
    distribution of building blocks within a genetic programming population and how this is changed by using simplification. We
    show that simplification results in reductions in expected program size, memory use and computation time. We also show that
    numerical simplification performs at least as well as algebraic simplification, and in some cases will outperform algebraic
    simplification. We further show that although the two on-line simplification methods destroy some existing building blocks,
    they effectively generate new more diverse building blocks during evolution, which compensates for the negative effect of
    disruption of building blocks.

    • Content Type Journal Article
    • DOI 10.1007/s12065-009-0029-9
    • Authors
      • David Kinzett, Victoria University of Wellington School of Engineering and Computer Science PO Box 600 Wellington New Zealand
      • Mark Johnston, Victoria University of Wellington School of Mathematics, Statistics and Operations Research PO Box 600 Wellington New Zealand
      • Mengjie Zhang, Victoria University of Wellington School of Engineering and Computer Science PO Box 600 Wellington New Zealand