Visualising and Solving a Maze Using an Artificial Intelligence Technique

M. N. Sagming, R. Heymann, E. Hurwitz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

This paper describes the implementation of an artificial intelligence (AI) technique known as Genetic Algorithm (GA), used to solve randomly generated mazes that are of varying sizes and complexity. To evaluate the effectiveness of the GA, several non-AI techniques are implemented such as Depth-First Search (DFS), Breadth-First Search (BFS), A-Star Algorithm (A∗), Dijkstra Algorithm (DA), and Greedy Best-First Search (GBFS). Genetic algorithm is a method for solving both constrained and unconstrained problems based on a natural selection process that mimics biological evolution. The non-AI algorithms make use of graph concepts together with underlying data structures to solve randomly generated mazes. This paper also shows the results obtained from five different experiments after implementing the complete system and executing each of the non-AI algorithms as well as the GA. The strongest results after executing each algorithm, that is, the number of steps and the time taken to solve the maze were recorded and graphs were plotted to compare the performance of the GA compared to the non-AI algorithms. The GA always found the shortest path but becomes slower than the non-AI algorithms for dimensions greater than 10x 10.

Original languageEnglish
Title of host publicationIEEE AFRICON 2019
Subtitle of host publicationPowering Africa's Sustainable Energy for All Agenda: The Role of ICT and Engineering, AFRICON 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728132891
DOIs
Publication statusPublished - Sept 2019
Event2019 IEEE AFRICON, AFRICON 2019 - Accra, Ghana
Duration: 25 Sept 201927 Sept 2019

Publication series

NameIEEE AFRICON Conference
Volume2019-September
ISSN (Print)2153-0025
ISSN (Electronic)2153-0033

Conference

Conference2019 IEEE AFRICON, AFRICON 2019
Country/TerritoryGhana
CityAccra
Period25/09/1927/09/19

Keywords

  • )
  • A-star (A
  • Artificial intelligence (AI)
  • binary encoding
  • crossover
  • depth-first search (DFS)
  • dijkstra algorithm (DA)
  • elitism count
  • fitness function
  • generation
  • genetic algorithm (GA) breadth-first search (BFS)
  • greedy best-first search (GBFS)
  • heuristic cost
  • individual
  • mutation
  • population
  • priority queue
  • queue
  • selection
  • smart agent
  • stack

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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