Investigating the Parameter Space of Evolutionary Algorithms
This work addresses parameter tuning challenges for practitioners using evolutionary algorithms, though it is incremental as it confirms existing intuitions about parameter robustness.
The researchers investigated how evolutionary algorithm parameters affect performance, finding through extensive experiments that parameter space contains many viable configurations for the studied problems.
The practice of evolutionary algorithms involves the tuning of many parameters. How big should the population be? How many generations should the algorithm run? What is the (tournament selection) tournament size? What probabilities should one assign to crossover and mutation? Through an extensive series of experiments over multiple evolutionary algorithm implementations and problems we show that parameter space tends to be rife with viable parameters, at least for 25 the problems studied herein. We discuss the implications of this finding in practice.