Evolutionary and Population-Based Search

Mon, 01 Jan 0001 00:00:00 +0000

Evolutionary and Population-Based Search #

Population-based search explores with many candidate solutions at once. Genetic Algorithms evolve encoded solutions, Ant Colony Optimisation builds paths using collective pheromone signals, and neuroevolution applies evolutionary ideas to neural-network design.

Genetic Algorithm
Ant Colony Optimisation
Neural Architecture Search
Use case : Neuro Evolution - Evolutionary computation to design or train neural networks

Learning Objectives #

explain the purpose of population-based search
describe representation, fitness, selection, crossover and mutation
trace the core Genetic Algorithm loop and one numerical iteration
explain exploration and exploitation in Ant Colony Optimisation
interpret the main ACO transition and pheromone-update equations
distinguish NAS, NEAT, DeepNEAT and CoDeepNEAT

Big Picture #

flowchart TD
 P["Population-based search"] --> G["Genetic Algorithms"]
 P --> A["Ant Colony Optimisation"]
 G --> N["Neuroevolution"]
 N --> D["Deep network design"]

 style P fill:#E1F5FE
 style G fill:#C8E6C9
 style A fill:#FFF9C4
 style N fill:#EDE7F6
 style D fill:#C8E6C9

1. Why Use a Population? ☆ #

A single-state method follows one candidate through the search space. A population-based method maintains several candidates in parallel.

Neuroevolution on Arshad Siddiqui

Evolutionary and Population-Based Search

Evolutionary and Population-Based Search #

Learning Objectives #

Big Picture #

1. Why Use a Population? ☆ #