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 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 ...

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 ...

Smart contracts are programs that reside on a block-chain. A key feature of smart contracts is their immutability, meaning that they cannot be modified once they are deployed. Despite existing efforts to uncover vulnerabilities, a common assumption is ...

Software testing is a key component of the software engineering process, but modern software is highly complex. Software configurations involve many interacting components and interactions among them can strongly affect the software's behavior in hard-to-...

We evolve survivors for the CodeGuru competition --- assembly programs that run the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolving top-notch solvers, we specify a Backus Normal Form (BNF)...

A phylogeny describes a population's evolutionary history. Evolutionary search algorithms can perfectly track the ancestry of candidate solutions, illuminating a population's trajectory through the search space. We introduce phylogeny-informed ...

Genetic programming systems typically require large computational resource investments for training-set evaluations. Down-sampling these sets has proven to decrease costs and improve problem-solving success, particularly with the lexicase parent ...

Large Language Models (LLMs) have garnered significant attention due to their impressive capabilities. However, leveraging LLMs can be expensive due to the computational resources required, with costs depending on invocation numbers and input prompt ...