We propose a novel type of Artificial Immune System (AIS): Symbiotic Artificial Immune Systems (SAIS), drawing inspiration from symbiotic relationships in biology. SAIS parallels the three key stages (i.e., mutualism, commensalism and parasitism) of ...

Genetic Programming (GP) uses Darwinian evolution to generate algorithms for tasks such as classification and symbolic regression. However, a drawback is the interpreter used to evaluate candidate programs adding significant computational cost. Hence, ...

We explore variable-length metaheuristics for optimizing sets of rules for regression tasks by extending an earlier short paper that performed a preliminary analysis of several variants of a single-objective Genetic Algorithm. We describe more in depth ...

Recent work has argued that many machine learning techniques exhibit a 'double descent' in model risk, where increasing model complexity beyond an interpolation zone can overcome the bias-variance tradeoff to produce large, over-parameterised models that ...

This paper introduces a novel classification method that utilizes genetic programming (GP). The primary purpose of the proposed method is to enhance future generations of GP, through continuously refining the genetic makeup of the population for improved ...

Repairing bugs can be a daunting task for even a human experienced in debugging, so naturally, attempting to automatically repair programs with a computer system is quite challenging. The existing methods of automated program repair leave a lot of room ...

This work provides a modification to the Evolutionary eXploration of Augmenting Memory Models (EXA-GP) graph-based genetic programming (GGP) algorithm, enabling it to produce time series forecasting (TSF) equations that are vastly more simple and ...

This study proposes a genetic programming (GP) approach for classification, integrating cooperative co-evolution with multi-view synthesis. Addressing the challenges of high-dimensional data, we enhance GP by distributing features across multiple ...

Genetic Programming (GP) presents a unique challenge in fitness evaluation due to the need to repeatedly execute the evolved programs, often represented as tree structures, to assess their quality on multiple input data. Traditional approaches rely on ...

Machine learning (ML) is revolutionising drug discovery by expediting the prediction of small molecule properties essential for developing new drugs. These properties - including absorption, distribution, metabolism and excretion (ADME) - are crucial in ...

Learning classifier systems (LCSs) are a state-of-the-art methodology for developing rule-based machine learning by applying discovery algorithms and learning components. LCSs have become proficient at linking environmental features to describe simple ...

In this paper we explore the reconstruction of a delay differential equation (DDE) model from economics, the Kalecki's business cycle model, with a variant of genetic programming (GP) encoding the structure of DDEs. The results of this preliminary work ...

The explicit vectorization of the mathematical operations required for fitness calculation can dramatically increase the efficiency of tree-based genetic programming for symbolic regression. In this paper, we introduce a modern software design for the ...

de novo drug design (dnDD) aims to generate novel molecules that meet several conflicting objectives, positioning it as a quintessential many-objective optimization problem (MaOP), where more than three objectives must be simultaneously optimized. This ...

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a state of the art evolutionary algorithm that leverages linkage learning to efficiently exploit problem structure. By identifying and preserving important building blocks during variation, ...

The abstraction and reasoning corpus (ARC) is a challenging AI benchmark as it requires models to learn unseen relationships from a few data points. Each puzzle only contains 2--5 training examples, which makes it hard for models that require training on ...

This paper concerns explaining session-based recommendations using Grammatical Evolution. A session-based recommender system processes a given sequence of products browsed by a user and suggests the most relevant next product to display to the user. ...

Applying optimisation techniques such as evolutionary computation to real-world tasks often requires significant adaptation. However, specific application domains do not typically demand major changes to existing optimisation methods. The decisive aspect ...

Deep learning neuroarchitecture and hyperparameter search are important in finding the best configuration that maximizes learned model accuracy. However, the number of types of layers, their associated hyperparameters, and the myriad of ways to connect ...

The eXtended Classifier System (XCS) is the most widely studied classifier system in the community. It is a class of interpretable AI which has shown strong capability to master various classification and regression tasks. It has also shown strong ...