June 21, 2009

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

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© 2006 Kumaravel & Project Team

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