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Genetic Algorithms

Genetic algorithms are a powerful search technique that makes use of a biological metaphor: a set of candidate solutions is encoded as a population of chromosomes, and genetic operators such as crossover and mutation are applied to create a new population of solutions. This technique is capable of performing an efficient parallel search of very sparse search spaces. With the addition of a final local search phase, such as hill-climbing, effective solutions to hard practical problems can be achieved.

Applications of genetic algorithms include timetabling, scheduling, and adapting models of complex systems to fit new data. Recent work has shown that search control knowledge that controls planning strategy can be learned using genetic algorithm techniques.

Projects Publications Contact
Genetic Algorithms and Genetic Programming Learning Action Strategies for Planning Domains using Genetic Programming Austin Tate

John Levine is now at University of Strathclyde

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Updated: Tue Feb 8 10:06:12 2011
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