In this paper, two types of fitness landscapes of the graph bipartitioning problem are
analyzed, and a memetic algorithm—a genetic algorithm incorporating local search—that …

Interactions between decision variables typically make an optimization problem challenging
for an evolutionary algorithm (EA) to solve. Exploratory landscape analysis (ELA) techniques …

The minimum load coloring problem (MLCP) is an important NP-complete problem arising in
Wavelength Division Multiplexing (WDM) with a wide application in broadcast WDM …

Artificial benchmark functions are commonly used in optimization research because of their
ability to rapidly evaluate potential solutions, making them a preferred substitute for real …

Exploratory landscape analysis and fitness landscape analysis in general have given
valuable insight into problem hardness understanding as well as facilitating algorithm …

Many problems of practical and theoretical importance within the fields of Artificial
Intelligence and Operations Research are of a combinatorial nature. Combinatorial …

The thesis is about linear genetic programming (LGP), a machine learning approach that
evolves computer programs as sequences of imperative instructions. Two fundamental …

A number of fitness landscape analysis approaches are based on random walks through
discrete search spaces. Applying these approaches to real-encoded problems requires the …

This paper presents an approach to regularization of inductive genetic programming tuned
for learning polynomials. The objective is to achieve optimal evolutionary performance when …

Given a graph G, a proper k-coloring of G is an assignment of k colors {1,…, k} to the vertices
of G such that two adjacent vertices receive two different colors. The minimum sum coloring …