Tag: Genetic Algorithms

Course content

• Inspiration
• Gene Representation and Fitness Function
• Selection
• Recombination (crossover)
• Mutation
• Elitism
• Implementation of the Genetic Algorithm
• Continuous version of the Genetic Algorithm
• Applications of the Genetic Algorithm
• Bonus Video

Course content

• Introduction
• Genetic Algorithms
• Differential Evolution
• Artificial Neural Networks
• Clonal Selection Algorithm
• Particle Swarm Optimization
• Ant Colony Optimization
• Final remarks

Course content

• Installation
• Get the materials
• Genetic Algorithms Intuition
• Genetic Algorithms Practical
• a-Learning
• a-Learning Practical
• Deep a-Learning with ANNs
• Deep a-Learning Practical
• Deep Convolutional Q-Learning
• Deep Convolutional a-Learning Practical
• ANNEX 1: Artificial Neural Networks
• ANNEX 2: Convolutional Neural Networks
• Congratulations!! Don’t forget your Prize 🙂

Duration

28 hours (usually 4 days including breaks)

Requirements

Basic understanding of search problems and optimization

Overview

This four day course is aimed at teaching how genetic algorithms work; it also covers how to select model parameters of a genetic algorithm; there are many applications for genetic algorithms in this course and optimization problems are tackled with the genetic algorithms.

Course Outline

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