Tomoyuki HIROYASU Mitsunori MIKI Masahiro HAMASAKI Yusuke TANIMURA

Slides:

Advertisements

Similar presentations

Using Parallel Genetic Algorithm in a Predictive Job Scheduling

Advertisements

Divided Range Genetic Algorithms in Multiobjective Optimization Problems Tomoyuki HIROYASU Mitsunori MIKI Sinya WATANBE Doshisha University.

Fractal Element Antenna Genetic Optimization Using a PC Cluster ACES Proceedings March 21, 2002 Monterey, CA.

1 Distributed Computing Algorithms CSCI Distributed Computing: everything not centralized many processors.

Optimization and evaluation of parallel I/O in BIPS3D parallel irregular application Performance Modelling, Evaluation, and optimization of Parallel and.

PGA – Parallel Genetic Algorithm Hsuan Lee. Reference  E Cantú-Paz, A Survey on Parallel Genetic Algorithm, Calculateurs Paralleles, Reseaux et Systems.

Genetic Algorithm What is a genetic algorithm? “Genetic Algorithms are defined as global optimization procedures that use an analogy of genetic evolution.

1 Reasons for parallelization Can we make GA faster? One of the most promising choices is to use parallel implementations. The reasons for parallelization.

SBSE Course 4. Overview: Design Translate requirements into a representation of software Focuses on –Data structures –Architecture –Interfaces –Algorithmic.

Parallel Genetic Algorithms with Distributed-Environment Multiple Population Scheme M.Miki T.Hiroyasu K.Hatanaka Doshisha University,Kyoto,Japan.

A New Model of Distributed Genetic Algorithm for Cluster Systems: Dual Individual DGA Tomoyuki HIROYASU Mitsunori MIKI Masahiro HAMASAKI Yusuke TANIMURA.

Neural and Evolutionary Computing - Lecture 10 1 Parallel and Distributed Models in Evolutionary Computing  Motivation  Parallelization models  Distributed.

MOGADES: Multi-Objective Genetic Algorithm with Distributed Environment Scheme Intelligent Systems Design Laboratory ， Doshisha University ， Kyoto Japan.

Doshisha Univ. JapanGECCO2002 Energy Minimization of Protein Tertiary Structure by Parallel Simulated Annealing using Genetic Crossover Takeshi YoshidaTomoyuki.

Cluster Workstations. Recently the distinction between parallel and distributed computers has become blurred with the advent of the network of workstations.

Beowulf Cluster Jon Green Jay Hutchinson Scott Hussey Mentor: Hongchi Shi.

Optimization Problems - Optimization: In the real world, there are many problems (e.g. Traveling Salesman Problem, Playing Chess ) that have numerous possible.

Doshisha Univ., Japan Parallel Evolutionary Multi-Criterion Optimization for Block Layout Problems ○ Shinya Watanabe Tomoyuki Hiroyasu Mitsunori Miki Intelligent.

Robin McDougall Scott Nokleby Mechatronic and Robotic Systems Laboratory 1.

The Generational Control Model This is the control model that is traditionally used by GP systems. There are a distinct number of generations performed.

Distributed Genetic Algorithms with a New Sharing Approach in Multiobjective Optimization Problems Tomoyuki HIROYASU Mitsunori MIKI Sinya WATANABE Doshisha.

Doshisha Univ., Kyoto, Japan CEC2003 Adaptive Temperature Schedule Determined by Genetic Algorithm for Parallel Simulated Annealing Doshisha University,

A Parallel Genetic Algorithm with Distributed Environment Scheme

Particle Swarm Optimization † Spencer Vogel † This presentation contains cheesy graphics and animations and they will be awesome.

Parallel Genetic Algorithms By Larry Hale and Trevor McCasland.

Tamaki Okuda ● Tomoyuki Hiroyasu 　 Mitsunori Miki 　 Shinya Watanabe 　

Genetic Search Algorithms Matt Herbster. Why Another Search?  Designed in the 1950s, heavily implemented under John Holland (1970s)  Genetic search.

Doshisha Univ., Kyoto Japan NCGA : Neighborhood Cultivation Genetic Algorithm for Multi-Objective Optimization Problems Intelligent Systems Design Laboratory，

- Divided Range Multi-Objective Genetic Algorithms -

Parallel Simulated Annealing using Genetic Crossover Tomoyuki Hiroyasu Mitsunori Miki Maki Ogura November 09, 2000 Doshisha University, Kyoto, Japan.

Genetic Algorithm(GA)

Chapter 1: Introduction What is an Operating System? Mainframe Systems Desktop Systems Multiprocessor Systems Distributed Systems Clustered System Real.

Evolutionary Computation Evolving Neural Network Topologies.

A MapReduced Based Hybrid Genetic Algorithm Using Island Approach for Solving Large Scale Time Dependent Vehicle Routing Problem Rohit Kondekar BT08CSE053.

CEng 713, Evolutionary Computation, Lecture Notes parallel Evolutionary Computation.

-A introduction with an example

Optimization of combinatorial problems with parallel hybrid evolutionary algorithms Tansel Dökeroğlu, Ph.D. First I would like to thank you for giving.

Using GA’s to Solve Problems

Chapter 1: Introduction

Balancing of Parallel Two-Sided Assembly Lines via a GA based Approach

Chapter 1: Introduction

Chapter 1: Introduction

Parallel Virtual Machine

Constructing a system with multiple computers or processors

Chapter 1: Introduction

Chapter 1: Introduction

Chapter 1: Introduction

Constructing a system with multiple computers or processors

Constructing a system with multiple computers or processors

Chapter 1: Introduction

Doshisha Univ., Kyoto Japan

Constructing a system with multiple computers or processors

Temperature Parallel Simulated Annealing with Adaptive Neighborhood for Continuous Optimization problem Mitsunori MIKI Tomoyuki HIROYASU Masayuki KASAI.

Subject Name: Operating System Concepts Subject Number:

○　Hisashi Shimosaka (Doshisha University)

MPJ: A Java-based Parallel Computing System

Distributed Computing:

Chapter 1: Introduction

Chapter 1: Introduction

New Crossover Scheme for Parallel Distributed Genetic Algorithms

Chapter 1: Introduction

Md. Tanveer Anwar University of Arkansas

Chapter 1: Introduction

Energy Minimization of Protein Tertiary Structure by Parallel Simulated Annealing using Genetic Crossover Doshisha University, Kyoto, Japan Takeshi Yoshida.

Types of Parallel Computers

Chapter 1: Introduction

Cluster Computers.

Mitsunori MIKI Tomoyuki HIROYASU Takanori MIZUTA

Presentation transcript:

A New Model of Parallel Distributed Genetic Algorithms for Cluster Systems: Dual Individual DGAs Tomoyuki HIROYASU Mitsunori MIKI Masahiro HAMASAKI Yusuke TANIMURA Doshisha University Kyoto, Japan

Aim of this study Optimization methods Genetic Algorithms Finding the best routings of the network Designing structures Constructing systems New model of DGAs Genetic Algorithms Dual Individual DGAs (DGAs) Island model (DGAs) Master Slave Cellular Easy to divide into tasks in several ways High searching ability

Dual Individual DGAs (DuDGAs) There are two individuals in each island High searching ability The high validity of the solutions because there are numbers of islands. Easiness to set up Crossover rate=1.0 Mutation rate= 0.5

Parallerization of DuDGAs Selection Crossover Island Mutation Evaluation In DuDGA, an island is moved by migraion.

Test functions and used parameters DuDGA and DGAs (4, 8, 12, 24 islands) are applied to each test function. F1=200bit Rastrigin F2=50bit Rosenbrock F3=100bit Griewank F4=100bit Ridge Number of islands 120 4,8,12,24 Population size 240 Migration rate 0.5 0.3 Migration interval 5 Crossover rate 1.0 Mutation rate 1/L Terminal condition After 5000 generation

Test Functions Rastrigin Griewank Ridge Rosenbrok

Cluster system for calculation Spec. of Cluster (16 nodes) Processor PentiumⅡ(Deschutes) Clock 400MHz # Processors 1 × 16 Main memory 128Mbytes × 16 Network Fast Ethernet (100Mbps) Communication TCP/IP, MPICH 1.1.2 OS Linux 2.2.10 Compiler gcc (egcs-2.91.61)

Searching ability (covering rate) Covering rate( it is the success rate of finding the optimum of each problem in 20 trials.) 率見 1.0 4 8 0.5 12 24 A DuDGA F1 F2 F3 F4 DuDGA has high searching ability.

The number of processors Distribution and parallel effects of DuDGAs 5 10 15 20 25 Speed Up Rate 1 5 10 15 The number of processors Speed up rate is the relation between the calculation time of one processor model and that of multi processor model. Therefore, this rate has the factor of the model distribution effects and the parallel effects of DuDGAs