Defining differences is a necessary prerequisite for finding diversity in solutions to a given problem. Diversity, in turn, is a property that is difficult to quantify, but is expected to bring favourable properties.

We introduce the signature transform ...

Two fundamental challenges face generative models in engineering applications: the acquisition of high-performing, diverse datasets, and the adherence to precise constraints in generated designs. We propose a novel approach combining optimization, ...

Cellular automata (CA) are a convenient way to describe the distributed evolution of a dynamical system over discrete time and space. They can be used to express evolutionary algorithms (EAs), where the time is the flow of iterations and the space is ...

With the development of fast and massively parallel evaluations in many domains, Quality-Diversity (QD) algorithms, that already proved promising in a large range of applications, have seen their potential multiplied. However, we have yet to understand ...

Optimizing a set of functions simultaneously by leveraging their similarity is called multi-task optimization. Current black-box multi-task algorithms only solve a finite set of tasks, even when the tasks originate from a continuous space. In this paper, ...

Graph-based Genetic Programming (GGP) can create interpretable control policies in graph form, but faces challenges such as local optima and solution fragility, which undermine its efficacy. Quality-Diversity (QD) has been effective in addressing similar ...

Human computation games can crowdsource human intuition and reasoning for complex problems. Collaboration on these problems may help players build on each others’ work, but may also reduce the variety of solutions due to convergence. Previous work has ...

The majority of standard approaches to financial portfolio optimization (PO) are based on the mean-variance (MV) framework. Given a risk aversion coefficient, the MV procedure yields a single portfolio that represents the optimal trade-off between ...

This work introduces a new lightweight and massively parallelizable implementation of a Quality-Diversity (QD) task: the libfastsim maze. This QD task involves finding a collection of neural network controllers navigating a robot to diverse positions ...

Quality-Diversity offers powerful ideas to create diverse, high-performing populations. Here, we investigate the capabilities these ideas hold to solve exploration-hard single-objective problems, in addition to creating diverse high-performing ...

This paper proposes a novel meta-learning approach to optimize a robust portfolio ensemble. The method uses a deep generative model to generate diverse and high-quality sub-portfolios combined to form the ensemble portfolio. The generative model ...

Learning algorithms, like Quality-Diversity (QD), can be used to acquire repertoires of diverse robotics skills. This learning is commonly done via computer simulation due to the large number of evaluations required. However, training in a virtual ...

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 ...

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 ...

Quality-Diversity (QD) algorithms have recently gained traction as optimisation methods due to their effectiveness at escaping local optima and capability of generating wide-ranging and high-performing solutions. Recently, Multi-Objective MAP-Elites (...

In the context of neuroevolution, Quality-Diversity algorithms have proven effective in generating repertoires of diverse and efficient policies by relying on the definition of a behavior space. A natural goal induced by the creation of such a ...

The synergies between Quality-Diversity (QD) and Deep Reinforcement Learning (RL) have led to powerful hybrid QD-RL algorithms that have shown tremendous potential, and bring the best of both fields. However, only a single deep RL algorithm (TD3) has ...

While Quality-Diversity algorithms attempt to produce a set of high quality solutions that are diverse throughout descriptor space, in reality decision makers are often interested in solutions with specific descriptor values. In this paper we suggest ...

How to jointly optimize the morphology and controller is a challenging problem in evolutionary robotics. Due to the large search space, both quality diversity algorithms and types of encodings have been employed to search the solution space more ...

Curriculum learning allows complex tasks to be mastered via incremental progression over 'stepping stone' goals towards a final desired behaviour. Typical implementations learn locomotion policies for challenging environments through gradual ...