Artificial Intelligence (AI) is a rapidly progressing field, achieving remarkable breakthroughs in areas ranging from computer vision and machine translation to world champion-level Go gameplay, autonomous vehicles, and Chat-GPT. The continuously ...

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 ...

The difficulty of learning optimal coefficients in regression models using only genetic operators has long been a challenge in genetic programming for symbolic regression. As a simple but effective remedy it has been proposed to perform linear scaling ...

Cognitive Diagnostic Models (CDMs) aim to analyze students' cognitive levels of each knowledge component (KC) by mining educational data. Existing CDMs can be mainly divided into two categories, i.e., traditional probability-based and neural-network-...

Neural architecture search (NAS) aims to automatically design suitable architectures of artificial neural networks (ANNs) under various situations. Recently, NAS based on zero-cost proxies can predict the performance of ANNs with the cost of a single ...

While off-policy reinforcement learning (RL) algorithms are sample efficient due to gradient-based updates and data reuse in the replay buffer, they struggle with convergence to local optima due to limited exploration. On the other hand, population-...

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 ...

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 ...

Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfully applied to a variety of domains and particularly in evolutionary ...

Understanding leakage is an important challenge within the water sector to minimise waste, energy use and carbon emissions. Every Water Distribution Network (WDN) has leakage, usually approximated as Minimum Night Flow (MNF) for each District Metered ...

Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space. However, in the presence of unpredictable noise, the fitness and descriptor of the same ...

Co-evolutionary algorithms have successfully trained agent teams for tasks such as autonomous exploration or robot soccer. However generally, such approaches seek a single strong team, whereas many real-world applications require agents to effectively ...

Genetic algorithms constitute a family of black-box optimization algorithms, which take inspiration from the principles of biological evolution. While they provide a general-purpose tool for optimization, their particular instantiations can be heuristic ...

In human beings, the joint development of the body and cognitive system has been shown to facilitate the acquisition of new skills and abilities. In the literature, these natural principles have been applied to robotics with mixed results and ...

Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria. The majority of fair machine learning methods transform the error-fairness trade-off into a single ...

Current approaches to program synthesis with Large Language Models (LLMs) exhibit a "near miss syndrome": they tend to generate programs that semantically resemble the correct answer (as measured by text similarity metrics or human evaluation), but ...

For large-scale multi-objective evolutionary algorithms (LSMOEAs), it has been a major challenge to efficiently obtain accurate evolutionary directions in the ultra-high-dimensional decision space to produce high-quality offspring. To this hand, this ...

The increasing number of objectives poses a great challenge upon many-objective optimization algorithms (MaOOAs) when solving many-objective optimization problems (MaOOPs), since it is rather difficult to obtain well-distributed solutions with tight ...