Local Search and Optimisation

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

Local Search and Optimisation #

Local search improves one or a few complete candidate solutions by moving through their neighbourhoods. It is especially useful when the final configuration matters but the path used to reach it does not.

– Local Search Algorithms & Optimisation Problems

Hill Climbing Search
Local Beam Search
Online Search Agents

Learning Objectives #

distinguish path optimisation from state optimisation
explain states, neighbours and objective functions
apply hill climbing and its variants
recognise local maxima, plateaus and ridges
explain random-restart and stochastic hill climbing
describe local and stochastic beam search and trace one beam update

Big Picture #

flowchart TD
 S["Current state"] --> N["Generate neighbours"]
 N --> E["Evaluate values"]
 E --> B["Choose candidate"]
 B --> S
 B --> G["Stop condition"]

 style S fill:#E1F5FE
 style N fill:#FFF9C4
 style E fill:#EDE7F6
 style B fill:#C8E6C9
 style G fill:#C8E6C9

1. Path Optimisation vs State Optimisation ☆ #

In classical route search, the path is the solution. The agent must remember how it travelled from the start to the goal.

Hill Climbing on Arshad Siddiqui

Local Search and Optimisation

Local Search and Optimisation #

Learning Objectives #

Big Picture #

1. Path Optimisation vs State Optimisation ☆ #