On the choice of the parameter control mechanism in the (1+(λ, λ)) genetic algorithm

The self-adjusting (1 + (λ, λ)) GA is the best known genetic algorithm for problems with a good fitness-distance correlation as in OneMax. It uses a parameter control mechanism for the parameter λ that governs the mutation strength and the number of offspring. However, on multimodal problems, the parameter control mechanism tends to increase λ uncontrollably.

We study this problem and possible solutions to it using rigorous runtime analysis for the standard Jumpk benchmark problem class. The original algorithm behaves like a (1+n) EA whenever the maximum value λ = n is reached. This is ineffective for problems where large jumps are required. Capping λ at smaller values is beneficial for such problems. Finally, resetting λ to 1 allows the parameter to cycle through the parameter space. We show that this strategy is effective for all Jumpk problems: the (1 + (λ, λ)) GA performs as well as the (1 + 1) EA with the optimal mutation rate and fast evolutionary algorithms, apart from a small polynomial overhead.

Along the way, we present new general methods for bounding the runtime of the (1 + (λ, λ)) GA that allows to translate existing runtime bounds from the (1 + 1) EA to the self-adjusting (1 + (λ, λ)) GA. Our methods are easy to use and give upper bounds for novel classes of functions.