Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Benchmarking real-coded genetic algorithm on noisy black-box optimization testbed
Proceedings of the 12th 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
Benchmarking cellular genetic algorithms on the BBOB noiseless testbed
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Genetic algorithms--a class of stochastic population-based optimization techniques--have been widely realized as the effective tools to solve complicated optimization problems arising from the diverse application domains. Originally developed version was a genetic algorithm with the binary representation of candidate solutions (i.e. chromosomes), the real-coded versions are, however, basically superior and frequently utilized in tackling complex real-valued optimization tasks. In this paper, a real-coded genetic algorithm (RCGA), which employs an adaptive-range variant of the well-known non-uniform mutation, is furnished with a multiple independent restarts mechanism to benchmark the noise-free black-box optimization testbed. The maximum number of function evaluations for each run is set to 50000 times the search space dimension. For low search space dimensions, the algorithm shows encouraging results on several functions. Although the algorithm is unable to solve all the functions to the highest required accuracy, for each type of functions, some of them can be solved, especially to lower precision, with the dimension up to 40.