Parameter-less evolutionary search
Proceedings of the 10th annual conference on Genetic and evolutionary computation
General Layout of City Pedestrian Bridge
ICIII '08 Proceedings of the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 03
Futility-based offspring sizing
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An exploration into dynamic population sizing
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Is self-adaptation of selection pressure and population size possible?: a case study
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Using supportive coevolution to evolve self-configuring crossover
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Traditional evolutionary algorithms (EAs) are powerful problem solvers that have several fixed parameters which require tuning. An increasing body of evidence suggests that the optimal values of some, if not all, EA parameters change during the course of executing an evolutionary run. This paper investigates the potential benefits of dynamic parameters by applying a Meta-EA to evolving optimal dynamic parameter values for population size, offspring size, n in n-point crossover, Gaussian mutation's step size, bit flip mutation's mutation rate, parent selection tournament size, and survivor selection tournament size. Each parameter was optimized both as the only dynamic parameter, and with all parameters dynamic. The most effective two parameters when acting independently were also allowed to optimize in tandem. The results were compared with a Meta-EA tuned EA using static parameters on the DTrap, NK, Rastrigin, and Rosenbrock benchmark problems. Results support that all tested parameters have the potential to improve solution fitness by changing dynamically, and using multiple dynamic parameters was more effective than using each independently.