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 ...

Cartesian Genetic Programming (CGP) allows for the optimization of interpretable function representations. However, comprehending the vast and combinatorially complex search space inherent to CGP remains challenging, particularly because 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 ...

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 ...

We introduce GOLEM --- an open-source optimization framework for automated design of graph-based structures in various scientific domains. It solves the problem of finding optimal topology and parameters of graphs using evolutionary algorithms, and does ...

EasyLocal++ is a white-box C++ framework for designing local search algorithms. Over the years, it has been successfully used across various domains, such as timetabling, rostering, scheduling, and logistics, and has produced state-of-the-art results in ...

In the realm of Machine Learning, the pursuit of simpler yet effective models has led to increased interest in decision trees due to their interpretability and efficiency. However, their inherent simplicity often limits their ability to handle intricate ...

This article describes how to solve Sudoku puzzles using Quadratic Unconstrained Binary Optimization (QUBO). To this end, a QUBO instance with 729 variables is constructed, encoding a Sudoku grid with all constraints in place, which is then partially ...

We conduct a preliminary study exploring the potential of using fitness landscape analysis for understanding the evolution of malware. This type of optimisation is fairly new and has not previously been studied through the lens of landscape analysis. We ...

In this paper, we study the parallelization of hybrid evolutionary heuristic for solving multi-objective optimization problems on heterogeneous CPU-GPU multi-core architectures. Two independent heuristics, Non-dominated Sorting Genetic Algorithm II (NSGA-...

This article summarizes recent work in the field of Automated Program Repair that was published in Transactions on Evolutionary Learning and Optimization as Evolving Software: Combining Online Learning with Mutation-Based Stochastic Search. Automated ...

Deep Neural Networks (DNNs) are increasingly used for critical tasks, such as classification in autonomous driving, whose trustworthiness is extremely important. To guarantee trustworthiness, DNN repair has been used to improve DNN performance, by using ...

The non-deterministic nature of modern systems such as cyber-physical systems (e.g. due to sensor noise) and multi-process/multi-agent systems (e.g. due to timing differences), poses a significant challenge in the field of multi-objective optimisation (...

This short paper presents the main findings of our work titled Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving, which was recently published in the Evolutionary Computation Journal. In this ...

Advances in Large Language Models (LLMs) have led them to be used as black boxes in several evolutionary algorithms for program synthesis. While these methods tend to be agnostic about which model is used, they only allow for using one. This paper ...

Within the domain of Search-Based Software Testing (SBST), there is a growing research emphasis on hard branch coverage during the generation of test data. A considerable portion of this research strives to enhance the fitness function by integrating ...

Due to their versatility and increasing popularity, Large Language Models (LLMs) can offer exciting new research avenues in most fields of science and technology. This paper describes a proof-of-concept experiment on the applicability of LLMs as a ...

The problem of program synthesis involves automatically finding a function based on evaluation criteria, like matching input-output pairs. While Cartesian Genetic Programming (CGP) has excelled in various function synthesis tasks, it has primarily been ...

Real-Coded Genetic Algorithms (RCGAs) are a significant area of focus in evolutionary algorithm research. Structural bias (SB), a recently recognized attribute in metaheuristic algorithms, leads populations to repeatedly exploit specific region(s) of the ...

Artificial benchmark datasets are common in both numerical and discrete optimisation domains. Existing benchmarks cover a broad range of classes of optimisation, but as a general rule have limited value due to their poor resemblance to real-world ...