We expose and contrast the impact of landscape characteristics on the performance of
search heuristics for black-box multiobjective combinatorial optimization problems. A sound …

This chapter overviews a recently introduced network-based model of combinatorial
landscapes: Local Optima Networks (LON). The model compresses the information given by …

Boolean functions have numerous applications in domains as diverse as coding theory,
cryptography, and telecommunications. Heuristics play an important role in the construction …

Local Optima Networks are models proposed to understand the structure and properties of
combinatorial landscapes. The fitness landscape is explored as a graph whose nodes …

In this article, we attempt to understand and to contrast the impact of problem features on the
performance of randomized search heuristics for black-box multiobjective combinatorial …

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 …

Pareto local optimal solutions (PLOS) are believed to highly influence the dynamics and the
performance of multi-objective optimization algorithms, especially those based on local …

Connection patterns among Local Optima Networks (LONs) can inform heuristic design for
optimisation. LON research has predominantly required complete enumeration of a fitness …

Abstract Local Optima Networks (LON) model combinatorial landscapes as graphs, where
nodes are local optima and edges transitions among them according to given move …

A local optima network (LON) compresses relevant features of fitness landscapes in a
complex network, where nodes are local optima and edges represent transition probabilities …