A Parameterized Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms

TL;DR

This paper conducts a runtime analysis of evolutionary algorithms for bi-level optimisation problems, focusing on NP-hard problems like GMST and GTSP, and explores their fixed-parameter tractability based on problem structure.

Contribution

It provides the first parameterized complexity analysis of evolutionary algorithms applied to bi-level optimisation problems, revealing conditions for fixed-parameter efficiency.

Findings

Global structure representation enables fixed-parameter solutions for GMST.

(1+1) EA with global structure is fixed-parameter for GTSP.

The two approaches for GMST are highly complementary, solving each other's hard instances.

Abstract

Bi-level optimisation problems have gained increasing interest in the field of combinatorial optimisation in recent years. With this paper, we start the runtime analysis of evolutionary algorithms for bi-level optimisation problems. We examine two NP-hard problems, the generalised minimum spanning tree problem (GMST), and the generalised travelling salesman problem (GTSP) in the context of parameterised complexity. For the generalised minimum spanning tree problem, we analyse the two approaches presented by Hu and Raidl (2012) with respect to the number of clusters that distinguish each other by the chosen representation of possible solutions. Our results show that a (1+1) EA working with the spanning nodes representation is not a fixed-parameter evolutionary algorithm for the problem, whereas the global structure representation enables to solve the problem in fixed-parameter time. We present hard instances for each approach and show that the two approaches are highly complementary by proving that they solve each other's hard instances very efficiently. For the generalised travelling salesman problem, we analyse the problem with respect to the number of clusters in the problem instance. Our results show that a (1+1) EA working with the global structure representation is a fixed-parameter evolutionary algorithm for the problem.