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
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Scalability problems of simple genetic algorithms
Evolutionary Computation
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
An ACO algorithm benchmarked on the BBOB noiseless function testbed
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Benchmarking projection-based real coded genetic algorithm on BBOB-2013 noiseless function testbed
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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One of the earliest evolutionary computation algorithms, the genetic algorithm, is applied to the noise-free BBOB 2009 testbed. It is adapted to the continuous domain by increasing the number of bits encoding each variable, until a desired resolution is possible to achieve. Good results and scaling are obtained for separable functions, but poor performance is achieved on the other functions, particularly ill-conditioned functions. Overall running times remain fast throughout.