Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved. The complexity of these problems is well beyond the boundaries of applicability of exact optimisation algorithms ...

Plenty of inspiring runtime analysis results have been recently obtained for the (1 + (λ, λ)) genetic algorithm (GA) on different benchmark functions. These results showed the efficiency of this GA, but we still do not have much understanding of its ...

Structural Bias (SB) is an important type of algorithmic deficiency within iterative optimisation heuristics. However, methods for detecting structural bias have not yet fully matured, and recent studies have uncovered many interesting questions. One of ...

A major challenge with utilizing a metaheuristic is finding optimal or near optimal parameters for a given problem instance. It is well known that the best performing control parameters are often problem dependent, with poorly chosen parameters even ...

The most basic concept of (meta-)heuristic optimization is to prefer better solutions over worse ones. Algorithms utilizing Frequency Fitness Assignment (FFA) break with this idea and instead move towards solutions whose objective value has been ...

In real-world applications, many optimization problems have the time-linkage property, that is, the objective function value relies on the current solution as well as the historical solutions. Although the rigorous theoretical analysis on evolutionary ...

We use an elementary argument building on group actions to prove that the selection-free steady state genetic algorithm analyzed by Sutton and Witt (GECCO 2019) takes an expected number of [EQUATION] iterations to find any particular target search ...

Previous theory work on multi-objective evolutionary algorithms considers mostly easy problems that are composed of unimodal objectives. This paper takes a first step towards a deeper understanding of how evolutionary algorithms solve multi-modal multi-...

A new class of test functions for black box optimization is introduced. Affine OneMax (AOM) functions are defined as compositions of OneMax and invertible affine maps on bit vectors. The black box complexity of the class is upper bounded by a polynomial ...

To overcome some deficiencies of inverted generational distance (IGD) and hypervolume (HV), two comprehensive metrics are proposed in this paper, the hypercube distance (HCD), a metric based on hypercubes, and the angle-based distance (AD) for ...