Marker Gene Method : Identifying Stable Solutions in a Dynamic Environment
This addresses stability issues in CCEAs for researchers and practitioners in evolutionary computation, though it appears incremental as it builds on existing frameworks.
The paper tackles the problem of unstable convergence in Competitive Co-evolutionary Algorithms (CCEAs) due to dynamics like intransitivity and the Red Queen effect, introducing the Marker Gene Method (MGM) to establish stability, with empirical results showing stabilization in games like Rock-Paper-Scissors and performance improvements on benchmarks.
Competitive Co-evolutionary Algorithms (CCEAs) are often hampered by complex dynamics like intransitivity and the Red Queen effect, leading to unstable convergence. To counter these challenges, this paper introduces the Marker Gene Method (MGM), a framework that establishes stability by using a 'marker gene' as a dynamic benchmark and an adaptive weighting mechanism to balance exploration and exploitation. We provide rigorous mathematical proofs demonstrating that MGM creates strong attractors near Nash Equilibria within the Strictly Competitive Game framework. Empirically, MGM demonstrates its efficacy across a spectrum of challenges: it stabilizes the canonical Rock-Paper-Scissors game, significantly improves the performance of C-RMOEA/D on ZDT benchmarks, and, when augmented with a Memory Pool (MP) extension, it successfully tames the notoriously pathological Shapley Biased Game. This work presents a theoretically sound and empirically validated framework that substantially enhances the stability and robustness of CCEAs in complex competitive environments.