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-...

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 ...

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 ...

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 ...

Program synthesis is the process of generating a computer program following a set of specifications, which can be a high-level description of the problem and/or a set of input-output examples. The synthesis can be modeled as a search problem in which ...

A continued fraction expansion represents a real number as an expression obtained by iteratively extracting the largest whole number from its fractional part and inverting the remainder.

Continued Fraction Regression (CFR) is a method for ...

Evolutionary Reinforcement Learning arises from hybridizing the sample efficiency of policy gradient with the stability of evolutionary computation. Proximal Distilled Evolutionary Reinforcement Learning (PDERL) implements the hybridization by having ...

Robot swarms often exhibit emergent behaviors that are fascinating to observe; however, it is often difficult to predict what swarm behaviors can emerge under a given set of agent capabilities. We seek to efficiently leverage human input to automatically ...

To mine high-dimensional rules in Learning Classifier Systems (LCSs) through a reduction of the dimensionality of input data, this paper proposes a novel approach that indirectly learns the rules in the "latent space" based on the rewards of the ...

Due to computationally and/or financially costly evaluation, tackling expensive multi-objective optimization problems is quite challenging for evolutionary algorithms. One popular approach to these problems is building cheap surrogate models to ...