Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Biases in the crossover landscape
Proceedings of the third international conference on Genetic algorithms
Sizing populations for serial and parallel genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Using genetic algorithms to solve NP-complete problems
Proceedings of the third international conference on Genetic algorithms
Distributed genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Exact and Approximate Algorithms for Scheduling Nonidentical Processors
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Efficient Scheduling Algorithms for Real-Time Multiprocessor Systems
IEEE Transactions on Parallel and Distributed Systems
Using Genetic Algorithms to Schedule Distributed Tasks on a Bus-Based System
Proceedings of the 5th International Conference on Genetic Algorithms
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Optimization of road networks using evolutionary strategies
Evolutionary Computation
Scheduling of water distribution system rehabilitation using structured messy genetic algorithms
Evolutionary Computation
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Genetic Algorithms have been used to solve a wide variety of problems. They have proven to be of notable usefulness in solving optimization problems of all kinds. Because of this, I believe that Genetic Algorithms should be taught routinely in Algorithms and Algorithm Analysis classes. My experience has shown that adding instruction about the implementation of Genetic Algorithms enhances student understanding of approximation algorithms and does not take an unreasonable amount of time away from the other topics.SUN workstations supplied by National Science Foundation DUE-ILI grant #9651290 provided the computing environment used for the work reported here.