コース概要

Day 1:

  • What is a genetic algorithm?
  • Chromosome fitness
  • Choosing the random initial population
  • The crossover operations
  • A numeric optimzation example

Day 2

  • When to use genetic algorithm
  • Coding the gene
  • Local maximums and mutation operation
  • Population diversity

Day 3

  • The meaning and effect of each genetic algorithm parameter
  • Varying genetic parameters
  • Optimizing scheduling problems
  • Cross over and mutation for scheduling problems

Day 4

  • Optimizing program or set of rules
  • Cross over and mutation operations for optimizing programs
  • Creating a parallel model of the genetic algorithm
  • Evaluating the genetic algorithm
  • Applications of genetic algorithm

要求

Basic understanding of search problems and optimization

  28 時間
 

参加者の人数


開始

完了


Dates are subject to availability and take place between 10:00 and 17:00.
Open Training Courses require 5+ participants.

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