Probabilistic model-building Genetic Algorithms (PMBGAs) are a class of metaheuristics that evolve probability distributions favoring optimal solutions in the underlying search space by repeatedly sampling from the distribution and updating it according to promising samples. We provide a rigorous runtime analysis concerning the update strength, a vital parameter in PMBGAs such as the step size 1 / K in the so-called compact Genetic Algorithm (cGA) and the evaporation factor ρ in ant colony optimizers (ACO). While a large update strength is desirable for exploitation, there is a general trade-off: too strong updates can lead to unstable behavior and possibly poor performance. We demonstrate this trade-off for the cGA and a simple ACO algorithm on the well-known OneMax function. More precisely, we obtain lower bounds on the expected runtime of Ω(Kn+nlogn) and Ω(n/ρ+nlogn), respectively, suggesting that the update strength should be limited to 1/K,ρ=O(1/(nlogn)). In fact, choosing 1/K,ρ∼1/(nlogn) both algorithms efficiently optimize OneMax in expected time Θ(nlog n). Our analyses provide new insights into the stochastic behavior of PMBGAs and propose new guidelines for setting the update strength in global optimization.
- Ant colony optimization
- Estimation-of-distribution algorithms
- Genetic Algorithms
- Probabilistic model-building Genetic Algorithms
- Runtime analysis
- Theory of randomized search heuristics