Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Models for iterative global optimization
Models for iterative global optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Selected Papers from AISB Workshop on Evolutionary Computing
Isolating the benefits of respect
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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The preservation of common components has been recently isolated as a beneficial feature of genetic algorithms. One interpretation of this benefit is that the preservation of common components can direct the search process to focus on the most promising parts of the search space. If this advantage can be transferred from genetic algorithms, it may be possible to improve the overall effectiveness of other heuristic search techniques. To identify common components, multiple solutions are required - like those available from a set of parallel searches. Results with simulated annealing and the Traveling Salesman Problem show that the sharing of common components can be an effective method to coordinate parallel search.