Applications of Genetic Algorithms

• Automated design, including research on composite material design and multi-objective design of automotive components for crashworthiness, weight savings, and other characteristics.
  
• Automated design of mechatronic systems using bond graphs and genetic programming (NSF).
  
• Calculation of Bound States and Local Density Approximations.
  
  
• Configuration applications, particularly physics applications of optimal molecule configurations for particular systems like C60 (buckyballs).
  
• Container loading optimization.
  
• Code-breaking, using the GA to search large solution spaces of ciphers for the one correct decryption.
  
• Design of water distribution systems.
  
• Distributed computer network topologies.
  
• Electronic circuit design, known as Evolvable hardware.
  
• File allocation for a distributed system.
  
• Parallelization of GA/GP including use of hierarchical decomposition of problem domains and design spaces nesting of irregular shapes using feature matching and GA.
  
• Game Theory Equilibrium Resolution.
  
• Learning Robot behavior using Genetic Algorithms.
  
• Learning fuzzy rule base using genetic algorithms.
  
• Mobile communications infrastructure optimization.
  
• Molecular Structure Optimization (Chemistry).
  
• Multiple population topologies and interchange methodologies.
  
• Protein folding and protein/ligand docking.
  
• Plant floor layout.
  
• Scheduling applications, including job-shop scheduling. The objective being to schedule jobs in a sequence dependent or non-sequence dependent setup environment in order to maximize the volume of production while minimizing penalties such as tardiness.
  
• Software engineering
  
• Solving the machine-component grouping problem required for cellular manufacturing systems.
  
• Tactical asset allocation and international equity strategies.
  
• Timetabling problems, such as designing a non-conflicting class timetable for a large university.
  
• Training artificial neural networks when pre-classified training examples are not readily obtainable (neuroevolution).
  
• Traveling Salesman Problem.
  

References:
Genetic algorithm - Wikipedia, the free encyclopedia, last modified 00:36, 15 February 2006, http://en.wikipedia.org/wiki/ Genetic_algorithm
Related Posts:

• ABSTRACT
• INTRODUCTION
• JOB SHOP SCHEDULING
• Search Methods and Optimization Techniques
• Genetic Algorithm
• Mechanism
• Characteristics of GA

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© 2006 Kumaravel & Project Team