Genetic Algorithm and Their Applications to Scheduling

Slides:



Advertisements
Similar presentations
Genetic Algorithm.
Advertisements

CS6800 Advanced Theory of Computation
Exact and heuristics algorithms
Genetic Algorithms Contents 1. Basic Concepts 2. Algorithm
O PTIMAL P ATH P LANNING FOR M OBILE R OBOT -T RAILER S YSTEMS Team 22: Siwei Wang Xin Yu Xi Li.
Institute of Intelligent Power Electronics – IPE Page1 Introduction to Basics of Genetic Algorithms Docent Xiao-Zhi Gao Department of Electrical Engineering.
Monte Carlo Methods and the Genetic Algorithm Definitions and Considerations John E. Nawn MAT 5900 March 17 th, 2011.
COMP305. Part II. Genetic Algorithms. Genetic Algorithms.
Introduction to Genetic Algorithms Yonatan Shichel.
Genetic Algorithms and Their Applications John Paxton Montana State University August 14, 2003.
Genetic Algorithms (GAs)
Evolutionary Algorithms Simon M. Lucas. The basic idea Initialise a random population of individuals repeat { evaluate select vary (e.g. mutate or crossover)
Genetic Algorithm for Variable Selection
Genetic Algorithms: Solving the Traveling Salesman Problem Thomas Abtey SUNY Oswego.
COMP305. Part II. Genetic Algorithms. Genetic Algorithms.
Artificial Intelligence Genetic Algorithms and Applications of Genetic Algorithms in Compilers Prasad A. Kulkarni.
Intro to AI Genetic Algorithm Ruth Bergman Fall 2002.
7/2/2015Intelligent Systems and Soft Computing1 Lecture 9 Evolutionary Computation: Genetic algorithms Introduction, or can evolution be intelligent? Introduction,
Artificial Intelligence in Information Processing Genetic Algorithms by Theresa Kriese for Distributed Data Processing.
Prepared by Barış GÖKÇE 1.  Search Methods  Evolutionary Algorithms (EA)  Characteristics of EAs  Genetic Programming (GP)  Evolutionary Programming.
Genetic Algorithm.
Genetic Algorithms and Ant Colony Optimisation
Genetic algorithms Prof Kang Li
Genetic Algorithms by using MapReduce
Zorica Stanimirović Faculty of Mathematics, University of Belgrade
More on Heuristics Genetic Algorithms (GA) Terminology Chromosome –candidate solution - {x 1, x 2,...., x n } Gene –variable - x j Allele –numerical.
1 Genetic Algorithms “Genetic Algorithms are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations.
Genetic Algorithms K.Ganesh Reasearch Scholar, Ph.D., Industrial Management Division, Humanities and Social Sciences Department, Indian Institute of Technology.
Genetic Algorithms Siddhartha K. Shakya School of Computing. The Robert Gordon University Aberdeen, UK
Derivative Free Optimization G.Anuradha. Contents Genetic Algorithm Simulated Annealing Random search method Downhill simplex method.
© Negnevitsky, Pearson Education, Lecture 9 Evolutionary Computation: Genetic algorithms Introduction, or can evolution be intelligent? Introduction,
1 Genetic Algorithms and Ant Colony Optimisation.
Genetic Algorithms Przemyslaw Pawluk CSE 6111 Advanced Algorithm Design and Analysis
Genetic Algorithms Czech Technical University in Prague, Faculty of Electrical Engineering Ondřej Vaněk, Agent Technology Center ZUI 2011.
Genetic Algorithms CSCI-2300 Introduction to Algorithms
Genetic Algorithms What is a GA Terms and definitions Basic algorithm.
Introduction to Genetic Algorithm Principle: survival-of-the-fitness Characteristics of GA Robust Error-tolerant Flexible When you have no idea about solving.
Genetic Algorithms Abhishek Sharma Piyush Gupta Department of Instrumentation & Control.
Chapter 12 FUSION OF FUZZY SYSTEM AND GENETIC ALGORITHMS Chi-Yuan Yeh.
EE749 I ntroduction to Artificial I ntelligence Genetic Algorithms The Simple GA.
GENETIC ALGORITHMS Tanmay, Abhijit, Ameya, Saurabh.
Genetic Algorithms MITM613 (Intelligent Systems).
Genetic Search Algorithms Matt Herbster. Why Another Search?  Designed in the 1950s, heavily implemented under John Holland (1970s)  Genetic search.
Genetic Algorithms. Underlying Concept  Charles Darwin outlined the principle of natural selection.  Natural Selection is the process by which evolution.
Genetic Algorithm Dr. Md. Al-amin Bhuiyan Professor, Dept. of CSE Jahangirnagar University.
Genetic Algorithms and TSP Thomas Jefferson Computer Research Project by Karl Leswing.
Artificial Intelligence By Mr. Ejaz CIIT Sahiwal Evolutionary Computation.
Advanced AI – Session 6 Genetic Algorithm By: H.Nematzadeh.
Genetic Algorithms. Solution Search in Problem Space.
Genetic Algorithms And other approaches for similar applications Optimization Techniques.
Genetic Algorithm(GA)
Presented By: Farid, Alidoust Vahid, Akbari 18 th May IAUT University – Faculty.
Genetic Algorithm (Knapsack Problem)
-A introduction with an example
Introduction to Genetic Algorithms
Genetic Algorithms.
Genetic Algorithms.
Advanced Computing and Networking Laboratory
A Comparison of Simulated Annealing and Genetic Algorithm Approaches for Cultivation Model Identification Olympia Roeva.
Chapter 6: Genetic Algorithms
Artificial Intelligence Project 2 Genetic Algorithms
Genetic Algorithms CPSC 212 Spring 2004.
Genetic Algorithms overview
CS621: Artificial Intelligence
Genetic Algorithms CSCI-2300 Introduction to Algorithms
Genetic algorithms: case study
Traveling Salesman Problem by Genetic Algorithm
Steady state Selection
Population Based Metaheuristics
GA.
Presentation transcript:

Genetic Algorithm and Their Applications to Scheduling Hyungjun Park

Introduction Based on the ideas of evolution and natural selection Pioneered by John Holland in 1960’s

Biological Background Genes Natural Selection and Fitness Reproduction Crossover Mutation

Genetic Algorithm Start Repeat Until End Condition End

Traveling Salesman Problem [Start] Random routes + heuristic [Fitness] Inverse of distance [Selection] Roulette wheel selection [Crossover] Leave blanks then refill [Mutation] Interchange Parameter Value Population Size 1,000 Mutation 3% Elitism Yes End Condition 200 Iterations

GA Performance Weighted Tardiness Descent Methods AU DES, DESO GA outperforms # of jobs DES DESO GA 50 20.78% 21.02% 33.68% 100 19.70% 19.47% 33.71% 200 17.46% 17.70% 33.72% 500 14.89% 14.81% 31.76%

Q & A