Meta-evolved Empirical Evidence of the Effectiveness of Dynamic Parameters

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\u27s step size, bit flip mutation\u27s 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