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End users can benefit from automatic program synthesis in a variety of applications, many of which require the user to specify the program they would like to generate. Recent advances in genetic programming allow it to generate general purpose ...

Complex adversarial operations typically involve the allocation of finite resources to meet a set of objectives over a number of phases. This poses a challenge for AI-based strategy discovery. A strategy for one phase cannot be developed in isolation ...

This paper introduces a user-driven evolutionary algorithm based on Quality Diversity (QD) search. During a design session, the user iteratively selects among presented alternatives and their selections affect the upcoming results. We implement a ...

This paper presents an interactive genetic algorithm (IGA) approach for video digest suggestion. The goal of the approach is to personalize video digest, which is personalized summary of video, by using a method with less fatigue. This approach ...

Large language models (LLMs) have taken the scientific world by storm, changing the landscape of natural language processing and human-computer interaction. These powerful tools can answer complex questions and, surprisingly, perform challenging ...

We study the effects of injecting human-generated designs into the initial population of an evolutionary robotics experiment, where subsequent population of robots are optimised via a Genetic Algorithm and MAP-Elites. First, human participants interact ...

Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algorithm can search that latent space. Such Latent Variable Evolution (LVE) ...

The initial phase in real world engineering optimization and design is a process of discovery in which not all requirements can be made in advance, or are hard to formalize. Quality diversity algorithms, which produce a variety of high performing ...

Many complex real-world problems such as bin-packing are optimised using evolutionary computation (EC) techniques. Involving a human user during this process can avoid producing theoretically sound solutions that do not translate to the real world but ...

Compositional Pattern Producing Networks (CPPNs) are a generative encoding that has been used to evolve a variety of novel artifacts, such as 2D images, 3D shapes, audio timbres, soft robots, and neural networks. This paper takes systems that generate ...

Self-organizing construction is an emerging subdomain for on-site construction robots. This not only presents new challenges for robotics, but due to the stochasticity involved in such systems, impacts the modeling and prediction of resulting built ...

We investigate the possibilities of incorporating user feedback at run-time in an embodied evolution setting. User feedback in this case consists of a user ranking a small sample from the population in an ongoing evolutionary process according to some ...

Conversion optimization means designing a web interface so that as many users as possible take a desired action on it, such as register or purchase. Such design is usually done by hand, testing one change at a time through A/B testing, or a limited ...

Within software engineering, requirements engineering starts from imprecise and vague user requirements descriptions and infers precise, formalized specifications. Techniques, such as interviewing by requirements engineers, are typically applied to ...

Creating controllers for NPCs in video games is traditionally a challenging and time consuming task. Automated learning methods such as neuroevolution (i.e. evolving artificial neural networks) have shown promise in this context but they often require ...

QR code, a kind of two-dimensional barcode, is used as shortcuts from physical media to online information in addition to object recognition purpose. However, QR code has become visually unpleasant with the global popularization of QR code. This paper ...

An open problem in neuroevolution (i.e. evolving artificial neural networks) is to evolve complex cognitive behaviors that allow robots to adapt and learn from past experience. While previous studies on the evolution of cognitive behaviors have shown ...

We present data demonstrating that the application of interactive evolution and related techniques has been growing since the early 1990s. Much research has honed the technique for specific applications. In this paper, we explicitly consider the ...

Evolutionary algorithms have shown themselves to be useful interactive design tools. However, current algorithms only receive feedback about candidate fitness at the whole-candidate level. In this paper we describe a model-free method, using sensitivity ...

Currently, a large number of computing systems and user applications are focused on distributed and collaborative models for heterogeneous devices, exploiting cloud-based approaches and social networking. However, such systems have not been fully ...