I have had the privilege of involvement in this field from its early days. The result is a rather unique and comprehensive perspective on its development and growth. In this talk I use that perspective to highlight some important milestones, discuss ...

With apparently all research on estimation-of-distribution algorithms (EDAs) concentrated on pseudo-Boolean optimization and permutation problems, we undertake the first steps towards using EDAs for problems in which the decision variables can take ...

The ideal point is critical in the multi-objective optimization problem (MOP), which consists of the best value of each objective. It is widely used for normalizing the objective space and guiding the evolution of the population. Since the ideal point ...

Unconventional computing devices are increasingly of interest as they can operate in environments hostile to silicon-based electronics, or compute in ways that traditional electronics cannot. Mechanical computers, wherein information processing is a ...

Computing diverse solutions for a given problem, in particular evolutionary diversity optimisation (EDO), is a hot research topic in the evolutionary computation community. This paper studies the Boolean satisfiability problem (SAT) in the context of ...

A long-standing question in the evolutionary multi-objective (EMO) community is how to generate a good initial population for EMO algorithms. Intuitively, as the starting point of optimization, a good initial population can have positive effects on ...

Evolutionary algorithms are known to be robust to noise in the evaluation of the fitness. In particular, larger offspring population sizes often lead to strong robustness. We analyze to what extent the (1 + (Λ, Λ)) genetic algorithm is robust to ...

Metaheuristics are commonly used to solve complex and challenging problems, particularly in electrical system applications. Nevertheless, there is a colorful palette of metaheuristics to select from, which can be overwhelming for a practitioner with ...

In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal cliff benchmark with remarkable efficiency. With its O (n³) runtime, ...

In building practical applications of evolutionary computation (EC), two optimizations are essential. First, the parameters of the search method need to be tuned to the domain in order to balance exploration and exploitation effectively. Second, the ...

Since Genetic Programming (GP) has been proposed, several flavors of GP have arisen, each with their own strengths and limitations. Grammar-Guided and Strongly-Typed GP (GGGP and STGP, respectively) are two popular flavors that have the advantage of ...

Competitive co-evolutionary algorithms (CoEAs) do not rely solely on an external function to assign fitness values to sampled solutions. Instead, they use the aggregation of outcomes from interactions between competing solutions allowing to rank ...

A vital component of intelligent action is affordance detection: understanding what actions external objects afford the viewer. This requires the agent to understand the physical nature of the object being viewed, its own physical nature, and the ...

Generating new instances via evolutionary methods is commonly used to create new benchmarking data-sets, with a focus on attempting to cover an instance-space as completely as possible. Recent approaches have exploited Quality-Diversity methods to ...

Parameterized analysis provides powerful mechanisms for obtaining fine-grained insights into different types of algorithms. In this work, we combine this field with evolutionary algorithms and provide parameterized complexity analysis of evolutionary ...

This paper deals with policy learning for a team of heterogeneous robotic agents when the whole team shares a single reward. We address the problem of providing an accurate estimation of the contribution of each agent in tasks where coordination ...

Gray-box optimization requires user-supported information about inter-variable dependencies to propose more effective optimizers for hard combinatorial problems. In Black-box optimization, such information is unavailable. Therefore, the Gray-box ...

Real-world optimisation problems often involve dynamics, where objective functions may change over time. Previous studies have shown that evolutionary algorithms (EAs) can solve dynamic optimisation problems. Additionally, the use of diversity ...

Coverage path planning is one of the main challenges in precision farming. Here the goal is to compute a continuous optimal working path by combining individual tracks such that the entire field can be cultivated in the most efficient way. Various 2D ...

It is an ongoing debate whether and how comma selection in evolutionary algorithms helps to escape local optima. We propose a new benchmark function to investigate the benefits of comma selection: OneMax with randomly planted local optima, generated ...