Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

Neuroevolution in Games: State of the Art and Open Challenges

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyse the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The article also highlights important open research challenges in the field.Comment: - Added more references - Corrected typos - Added an overview table (Table 1

Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior

Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs.\ individual behavior has a strong impact on the ability of evolved teams of artificial agents to exhibit teamwork in challenging tasks. However, such research only made use of single-objective evolution. In contrast, when a multiobjective evolutionary algorithm is used, populations can be subject to individual-level objectives, team-level objectives, or combinations of the two. This paper explores the performance of cooperatively coevolved teams of agents controlled by artificial neural networks subject to these types of objectives. Specifically, predator agents are evolved to capture scripted prey agents in a torus-shaped grid world. Because of the tension between individual and team behaviors, multiple modes of behavior can be useful, and thus the effect of modular neural networks is also explored. Results demonstrate that fitness rewarding individual behavior is superior to fitness rewarding team behavior, despite being applied to a cooperative task. However, the use of networks with multiple modules allows predators to discover intelligent behavior, regardless of which type of objectives are used

Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution

Although automated test generation is common in many programming domains, games still challenge test generators due to their heavy randomisation and hard-to-reach program states. Neuroevolution combined with search-based software testing principles has been shown to be a promising approach for testing games, but the co-evolutionary search for optimal network topologies and weights involves unreasonably long search durations. In this paper, we aim to improve the evolutionary search for game input generators by integrating knowledge about human gameplay behaviour. To this end, we propose a novel way of systematically recording human gameplay traces, and integrating these traces into the evolutionary search for networks using traditional gradient descent as a mutation operator. Experiments conducted on eight diverse Scratch games demonstrate that the proposed approach reduces the required search time from five hours down to only 52 minutes

Evolutionary neural architecture search for deep learning

Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. DNNs are often not used to their full potential because it is difficult to determine what architectures and hyperparameters should be used. While several approaches have been proposed, computational complexity of searching large design spaces makes them impractical for large modern DNNs. This dissertation introduces an efficient evolutionary algorithm (EA) for simultaneous optimization of DNN architecture and hyperparameters. It builds upon extensive past research of evolutionary optimization of neural network structure. Various improvements to the core algorithm are introduced, including: (1) discovering DNN architectures of arbitrary complexity; (1) generating modular, repetitive modules commonly seen in state-of-the-art DNNs; (3) extending to the multitask learning and multiobjective optimization domains; (4) maximizing performance and reducing wasted computation through asynchronous evaluations. Experimental results in image classification, image captioning, and multialphabet character recognition show that the approach is able to evolve networks that are competitive with or even exceed hand-designed networks. Thus, the method enables an automated and streamlined process to optimize DNN architectures for a given problem and can be widely applied to solve harder tasks.Computer Science

Quality Diversity: Harnessing Evolution to Generate a Diversity of High-Performing Solutions

Evolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of satisfying quality emerges. At the same time, research in open-ended evolution (OEE) draws different lessons from nature, seeking to identify and recreate processes that lead to the type of perpetual innovation and indefinitely increasing complexity observed in natural evolution. New algorithms in EC such as MAP-Elites and Novelty Search with Local Competition harness the toolkit of evolution for a related purpose: finding as many types of good solutions as possible (rather than merely the single best solution). With the field in its infancy, no empirical studies previously existed comparing these so-called quality diversity (QD) algorithms. This dissertation (1) contains the first extensive and methodical effort to compare different approaches to QD (including both existing published approaches as well as some new methods presented for the first time here) and to understand how they operate to help inform better approaches in the future. It also (2) introduces a new technique for encoding neural networks for evolution with indirect encoding that contain multiple sensory or output modalities. Further, it (3) explores the idea that QD can act as an engine of open-ended discovery by introducing an expressive platform called Voxelbuild where QD algorithms continually evolve robots that stack blocks in new ways. A culminating experiment (4) is presented that investigates evolution in Voxelbuild over a very long timescale. This research thus stands to advance the OEE community\u27s desire to create and understand open-ended systems while also laying the groundwork for QD to realize its potential within EC as a means to automatically generate an endless progression of new content in real-world applications

Drive-Based Utility-Maximizing Computer Game Non-Player Characters

This research examines the emergence of the five-string fiddle in contemporary North American fiddle culture within the past ten years. By interacting with leading artistlevel practitioners, the research documents the evolution and impact of the instrument to date in exploring the possibilities the five-string fiddle presents for musical performance and innovation. North American vernacular music and, in particular, the contemporary fiddle playing landscape, exemplifies virtousic and innovative idiomatic technique and improvisation as central to an overarching musical explosion, evidenced in the music of many high level, multi-stylistic contemporary practitioners. Within contemporary American fiddle performance, it is compelling to observe how many of the most innovative and highly regarded players now perform on five-string fiddles. The research uses a qualitative research methodology, drawing on interviews conducted with seven leading American fiddle players, each of whom has adopted the five-string fiddle in their own musical practice. The participants represent a rich cross section of American fiddle culture. They emerged naturally during the course of the literature review, and in-depth listening research, as particularly relevant sample cases. All participants were identified as leading exponents of the diversities encompassed in American fiddle music, between them sharing extensive professional recording, performance and academic experience, and all playing on five-string instruments. The research is further illuminated through practice, reflecting on my own musical work in illustrating how I have personally adopted the five-string fiddle, drawing influence from the research in demonstrating some wider possibilities of the instrument. This enquiry is important as it addresses the lack of specific research to date regarding the five-string fiddle, despite the significanance it holds for some of American fiddle music\u27s leading exponents, and consequently, for fiddle music itself. Equally significant, is the role of the instrument in facilitating the performance of innovative extended instrumental techniques, in particular, the five-string fiddles association with the rhythmic/percussive \u27chop\u27 bow techniques, now, so conspicuous within contemporary groove-based American string music. ix The findings of this research established the definitive emergence of the five-string fiddle, and subscribe that the five-string has now become a widely accepted part of the mainstream instrumentation in American music. This understanding emerges clearly through the words and practice of the participants. From this perspective, the research identifies the musical reasons that inspire the instruments popularity and elaborates through practice, the musical possibilities it presents to others. behaviour selection systems that have been used successfully in industry. The evaluations show that UDGOAP can outperform these systems in both environments. Another novel contribution of this thesis is smart ambiance. Smart ambiance is an area of space in a virtual world that holds information about the context of that space and uses this information to have non-player characters inside the space select more contextually appropriate actions. Information about the context comes from events that took place inside the smart ambiance, objects inside the smart ambiance, and the location of the smart ambiance. Smart ambiance can be used with any cost based planner. This thesis demonstrates dierent aspects of smart ambiance by causing an industry standard action planner to select more contextually appropriate behaviours than it otherwise would have without the smart ambiance