Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
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Genetic algorithms (GA) represent an algorithmic optimization technique inspired by biological evolution. A major strength of this meta-heuristic is its ability to explore the search space in independent parallel search routes rendering the algorithm highly efficient if implemented on a parallel architecture. Sequential simulations of GAs frequently result in enormous computational costs. To alleviate this problem, we propose a serial evolution strategy which results in a much smaller number of necessary fitness function evaluations thereby speeding up the computation considerably. If implemented on a parallel architecture the savings in computational costs are even more pronounced. We present the algorithm in full mathematical detail and proof the corresponding schema theorem for a simple case without cross-over operations. A toy example illustrates the operation of serial evolution and the performance improvement over a canonical genetic algorithm.