Quality-Diversity (QD) algorithms are a new type of Evolutionary Algorithms (EAs), aiming to
find a set of high-performing, yet diverse solutions. They have found many successful …

In recent years, computing diverse sets of high quality solutions for an optimization problem
has become an important topic. The goal of computing diverse sets of high quality solutions …

There has been a growing interest in the evolutionary computation community to compute a
diverse set of high-quality solutions for a given optimisation problem. This can provide the …

Evolutionary ensemble learning methods with Genetic Programming have achieved
remarkable results on regression and classification tasks by employing quality-diversity …

Generating instances of different properties is key to algorithm selection methods that
differentiate between the performance of different solvers for a given combinatorial …

A travelling thief problem (TTP) is a proxy to real-life problems such as postal collection. TTP
comprises an entanglement of a travelling salesman problem (TSP) and a knapsack …

Quality diversity (QD) algorithms have been shown to be very successful when dealing with
problems in areas such as robotics, games and combinatorial optimization. They aim to …

This study proposes Evolutionary Diversity Optimisation (EDO) to Lower the Probability of
Detection (LPD) in directed wireless networks. LPD communication aims to communicate …

Computing diverse solutions for a given problem, in particular evolutionary diversity
optimisation (EDO), is a hot research topic in the evolutionary computation community. This …

A plethora of combinatorial optimization problems can be linked to real-life decision
scenarios. Even nowadays, more diverse and complex problems are popping up. One of …