Exploratory Landscape Analysis for Meta-Learning-Based Algorithm Selection in Continuous Optimization

Continuous optimization now draws on a large and growing number of competing metaheuristic families, including differential-evolution lineages, covariance-matrix adaptation strategies, swarm-intelligence methods, and simulated annealing, yet no single method dominates across problem classes, a consequence of the No Free Lunch theorem. This motivates algorithm selection: choosing, for an unseen problem, the method most likely to perform best. Building on the OptimSolution framework, we assemble a headless, reproducible batch-execution pipeline coupled with Exploratory Landscape Analysis (ELA), enabling large-scale, statistically grounded benchmarking together with feature-based meta-learning. We evaluate a set of optimizers of comparable overall strength, drawn from distinct algorithmic families, across a broad benchmark suite spanning classical scalable functions and established competition test suites, under a consistent multi-run evaluation protocol. A Random Forest classifier trained on ELA features, validated under a Leave-One-Problem-Out cross-validation scheme, is shown to predict the best-performing optimizer for a previously unseen problem substantially more accurately than the naive Single Best Solver (SBS) baseline, closing a meaningful share of the gap toward the oracle Virtual Best Solver (VBS). Family-level analysis further shows that structured competition benchmarks and classical scalable functions tend to be won by different solvers, confirming that the underlying selection problem is genuine rather than trivial. These results indicate that landscape-aware selection can meaningfully outperform naive strategies when the candidate methods are drawn from heterogeneous algorithmic families of comparable strength.