Analysis of Baseline Evolutionary Algorithms for the Packing While Travelling Problem
This provides incremental theoretical insights for researchers in evolutionary computation by analyzing non-linear problems, which are less studied compared to linear ones.
The paper tackles the theoretical analysis of Evolutionary Algorithms (EAs) on non-linear combinatorial problems, specifically variations of the Packing While Travelling problem, showing that algorithms like RLS_swap find optimal solutions in O(n^3) expected time and (1+1) EA in O(n^2 log(max{n, p_max})) expected time for uniform weights.
The performance of base-line Evolutionary Algorithms (EAs) on combinatorial problems has been studied rigorously. From the theoretical viewpoint, the literature extensively investigates the linear problems, while the theoretical analysis of the non-linear problems is still far behind. In this paper, variations of the Packing While Travelling (PWT) -- also known as the non-linear knapsack problem -- are studied as an attempt to analyse the behaviour of EAs on non-linear problems from theoretical perspective. We investigate PWT for two cities and $n$ items with correlated weights and profits, using single-objective and multi-objective algorithms. Our results show that RLS\_swap, which differs from the classical RLS by having the ability to swap two bits in one iteration, finds the optimal solution in $O(n^3)$ expected time. We also study an enhanced version of GSEMO, which a specific selection operator to deal with exponential population size, and prove that it finds the Pareto front in the same asymptotic expected time. In the case of uniform weights, (1+1)~EA is able to find the optimal solution in expected time $O(n^2\log{(\max\{n,p_{\max}\})})$, where $p_{\max}$ is the largest profit of the given items. We also perform an experimental analysis to complement our theoretical investigations and provide additional insights into the runtime behavior.