Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a
growing research topic with many competitive bio-inspired algorithms being proposed every …

Heuristic search procedures that aspire to find global optimal solutions to hard combinatorial
optimization problems usually require some type of diversification to overcome local …

Over past few decades, as a powerful analytical tool to characterize the fitness landscape of
a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all …

Hyperparameter optimization (HPO) is a key component of machine learning models for
achieving peak predictive performance. While numerous methods and algorithms for HPO …

The Quantum Alternating Operator Ansatz (QAOA) is a prominent variational quantum
algorithm for solving combinatorial optimization problems. Its effectiveness depends on …

Neural architecture search is a promising area of research dedicated to automating the
design of neural network models. This field is rapidly growing, with a surge of methodologies …

The processing of features in data is among the key topics in machine learning. While a
broad range of heuristics for feature processing, including feature selection, have been …

In this paper, we propose a tunable generator of instances of permutation-based
combinatorial optimization problems. Our approach is based on a probabilistic model for …

Local optima networks (LONs) model the global distribution and connectivity pattern of local
optima under given search operators. Recent research has looked at how recombination …

We evaluate the performance of estimating the number of local optima by estimating their
proportion in the search space using simple random sampling (SRS). The performance of …