Blockchain has emerged as a subject of intense interest in research fields and beyond. One of key enabling technologies is smart contracts. Smart contracts bring transparency, simplicity, and efficiency to blockchain applications. Several languages and tools have been developed for smart contracts over the years but few are easily usable for domain experts in control systems as most of them are Simulink users and learning a new language and tool could be a challenge for them. In this paper, we propose a trusted smart contract development approach in Simulink Stateflow for blockchain applications. The approach introduces a design environment and generates evidences of trust for smart contracts via formal verification, simulation, automated test generation, and test execution. Finally, the formally verified Stateflow models is automatically synthesized into Solidity for deployment on blockchain platforms. This approach will not only bring the assurance of smart contracts to the next level, but also will make the blockchain technology accessible to the control experts, which could inspire broader blockchain applications in industry. We implemented the framework as a toolbox in Simulink, which has been used by GE Research. We will demonstrate the capabilities and effectiveness of the toolbox using a real transactive energy example to show that valid issues are identified along the development cycle.

This chapter provides an in-depth analysis of mission-critical applications in aviation that involve the decision triad of aircraft, Flight Operations Control (FOC), and the Air Navigation Service Provider (ANSP). A characterization of operations and system requirements is carried out to provide bases for identifying cyber-physical system (CPS) transformation opportunities. Transformation opportunities are examined from the perspectives of the aircraft, the FOC, and the ANSP respectively. Benefits provided by the transformation and the challenges faced by this transformation are discussed, with potential path for addressing them identified. It is concluded that CPS integration is already happening in some segments, and ultimately an industry-wide transformation can be achieved.

• With software development cost accounted for, a cloud transition would still save $2.8 to $7.3 million over the three year period. • Additionally, it will lead to a better system that will continue to save cost beyond the 3-year period, and it will be easier to upgrade and maintain.

A study of recent developments in the field of cloud computing indicates that, while challenges exist, with the proper alignment of technical and investment decisions, transitioning of Air Traffic Management functions to the cloud computing environment can be achieved much sooner than one would perceive, and that the transition is already happening for applications in air transportation. This paper demonstrates a framework for transitioning Air Traffic Management functions to cloud computing, for cost savings, and gains in performance and efficiency. An application modeled after a dynamic flow management system was used in a case study to identify required system changes and the associated cost. An analysis of the operating cost of the application revealed that significant savings can be achieved through this transition, along with the ability to benefit future upgrades and developments.

This article consists of a collection of slides from the author's conference presentation.

This paper presents a framework for transitioning Air Traffic Management (ATM) functions to cloud computing, for cost savings, and gains in performance and efficiency. With the established framework, initial analysis was carried out for NAS automation systems. The analysis revealed that it is technically feasible to transition most of the ATM functions to the cloud computing environment, with benefits significant to both the system owner and NAS end users. A study of recent development in the field also revealed that, while challenges exist, with the proper alignment of technical and investment decisions, transitioning of ATM functions to the cloud computing environment can be achieved much faster, and that it is already happening.

: GE Global Research and Massachusetts Institute of Technology jointly developed a crowdsourcing platform to support DARPA s vehicleforge.mil program to revolutionize modern-day design and manufacturing. Our vision is to build a crowd-driven ecosystem for evolutionary design (CEED). It will connect data, design tools and simulations in a collaborative environment to accelerate the design of highly complex industrial systems. Such an environment allows designers to team up to develop projects concurrently. Developers from different spaces will be able to form design communities and create a common project space. This space will allow them to manage processes as a team and track changes and updates on their project. Furthermore, the CEED platform provides a marketplace where contributors can choose to expose their ideas to the public either as open source or as IP protected services. The exposed design models, or models available for review in this open community, will experience market pressures that ultimately allow for the best designs to emerge. The CEED platform also embeds social media connections to maximize crowd engagement.

Quorum sensing (QS) is a collective behavior whereby actions of individuals depend on the density of the surrounding population. Bacteria use QS to trigger secretion of digestive enzymes, formation and destruction of biofilms, and, in the case of pathogenic organisms, expression of virulence factors that cause disease. Investigations of mechanisms that prevent or disrupt QS, referred to as quorum quenching, are of interest because they provide a new alternative to antibiotics for treating bacterial infections. Traditional antibiotics either kill bacteria or inhibit their growth, producing selective pressures that promote resistant strains. In contrast, quorum quenching and other so-called anti-infective strategies focus on altering behavior. In this article we evolve QS in populations of digital organisms, a type of self-replicating computer program, and investigate the effects of quorum quenching on these populations. Specifically, we injected the populations with mutant organisms that were impaired in selected ways to disrupt the QS process. The experimental results indicate that the rate at which these mutants are introduced into a population influences both the evolvability of QS and the persistence of an existing QS behavior. Surprisingly, we also observed resistance to quorum quenching. Effectively, populations evolved resistance by reaching quorum at lower cell densities than did the parent strain. Moreover, the level of resistance was highest when the rate of mutant introduction increased over time. These results show that digital organisms can serve as a model to study the evolution and disruption of QS, potentially informing wet-lab studies aimed at identifying targets for anti-infective development.

Evolutionary algorithms typically use direct encodings, where each element of the phenotype is specified independently in the genotype. Because direct encodings have difficulty evolving modular and symmetric phenotypes, some researchers use indirect encodings, wherein one genomic element can influence multiple parts of a phenotype. We have previously shown that HyperNEAT, an indirect encoding, outperforms FT-NEAT, a direct-encoding control, on many problems, especially as the regularity of the problem increases. However, HyperNEAT is no panacea; it had difficulty accounting for irregularities in problems. In this paper, we propose a new algorithm, a Hybridized Indirect and Direct encoding (HybrID), which discovers the regularity of a problem with an indirect encoding and accounts for irregularities via a direct encoding. In three different problem domains, HybrID outperforms HyperNEAT in most situations, with performance improvements as large as 40%. Our work suggests that hybridizing indirect and direct encodings can be an effective way to improve the performance of evolutionary algorithms.

Agenda • Introduction to digital evolution and Avida • Overview of The Evolutionary Origins of Complex Features • What is available and where to get it?

HyperNEAT represents a class of neuroevolutionary algorithms that captures some of the power of natural development with a computationally efficient high-level abstraction of development. This class of algorithms is intended to provide many of the desirable properties produced in biological phenotypes by natural developmental processes, such as regularity, modularity and hierarchy. While it has been previously shown that HyperNEAT produces regular artificial neural network (ANN) phenotypes, in this paper we investigated the open question of whether HyperNEAT can produce modular ANNs. We conducted such research on problems where modularity should be beneficial, and found that HyperNEAT failed to generate modular ANNs. We then imposed modularity on HyperNEAT's phenotypes and its performance improved, demonstrating that modularity increases performance on this problem. We next tested two techniques to encourage modularity in HyperNEAT, but did not observe an increase in either modularity or performance. Finally, we conducted tests on a simpler problem that requires modularity and found that HyperNEAT was able to rapidly produce modular solutions that solved the problem. We therefore present the first documented case of HyperNEAT producing a modular phenotype, but our inability to encourage modularity on harder problems where modularity would have been beneficial suggests that more work is needed to increase the likelihood that HyperNEAT and similar algorithms produce modular ANNs in response to challenging, decomposable problems.

Understanding the evolution of cooperation as part of an evolutionary stable strategy (ESS) is a difficult problem that has been the focus of much work. The associated costs of cooperation may lower the fitness of an organism below that of its non-cooperating counterpart, allowing the more fit organism to persist and outcompete the cooperator. Insight into these behaviors can help provide a better understanding of many aspects of the natural world, as well as provide future avenues for fighting disease. In this study, we use digital evolution to examine how the abundance of a required resource affects the cooperative production of a public good in an adverse environment. Evolutionary computation is an excellent tool for examining these problems, as it offers researchers complete access to organisms and total control over their environment. We find that stable cooperation can occur in otherwise competitive environments at discrete levels corresponding to the availability of a required resource. When resource levels are low, organisms focus solely on competitive behaviors. However, once resource levels cross a critical threshold, cooperation persists in populations. Further, this cooperation occurs in patches, where it is most likely to benefit relatives. Finally, we find that in some cases this cooperative behavior allows organisms to increase their competitive abilities as well.

Increasingly, high-assurance software systems apply self-reconfiguration in order to satisfy changing functional and non-functional requirements. Most self-reconfiguration approaches identify a target system configuration to provide the desired system behavior, then apply a series of reconfiguration instructions to reach the desired target configuration. Collectively, these reconfiguration instructions define an adaptation path. Although multiple satisfying adaptation paths may exist, most self-reconfiguration approaches select adaptation paths based on a single criterion, such as minimizing reconfiguration cost. However, different adaptation paths may represent tradeoffs between reconfiguration costs and other criteria, such as performance and reliability. This paper introduces an evolutionary computation-based approach to automatically evolve adaptation paths that safely transition an executing system from its current configuration to its desired target configuration, while balancing tradeoffs between functional and non-functional requirements. The proposed approach can be applied both at design time to generate suites of adaptation paths, as well as at run time to evolve safe adaptation paths to handle changing system and environmental conditions. We demonstrate the effectiveness of this approach by applying it to the dynamic reconfiguration of a collection of remote data mirrors, with the goal of minimizing reconfiguration costs while maximizing reconfiguration performance and reliability.

As software developers, we strive to create computational systems that are as robust and versatile as biological organisms have evolved to be in nature. We propose a software development methodology capable of producing self-adaptive software, using digital evolution to discover behaviors and optimize solutions. Employing this methodology we present an example behavioral concept from inception to fruition on physical hardware, as a proof of concept of the approach. We evolve environmentally-aware motility behaviors through digital evolution, automatically translate the evolved programs into C code, and compile and load the programs onto mobile robots.

The use of chemicals to communicate among organisms has enabled countless species, from microorganisms, to colonies of insects, to mammals, to survive and flourish in their respective environments. Ants, arguably nature's most successful exploiters of this behavior, have evolved the use of pheromones to communicate in a wide range of situations, including mating, colony recognition, territory marking, and recruitment to new nest sites and food sources. We examine the evolution of the use of pheromones to aid in the location of, and migration to, a target area by groups of digital organisms. In an initial set of experiments, these organisms evolved efficient patterns of exploration that obviated the need for pheromones. When evolved in a more adverse environment, organisms again evolved effective search strategies, but also evolved the use of pheromones to enable the task to be completed by group members more quickly and with fewer movements. We also show that evolved organisms are more robust and better able to react to a change in the environment than a handbuilt solution. This work demonstrates the complexities that exist in the evolution of pheromone-enabled cooperation and provides insight into the behaviors executed by seemingly simple organisms in nature.

For centuries it was thought that bacteria live asocial lives. However, recent discoveries show many species of bacteria communicate in order to perform tasks previously thought to be limited to multicellular organisms. Central to this capability is quorum sensing, whereby organisms detect cell density and use this information to trigger group behaviors. Quorum sensing is used by bacteria in the formation of biofilms, secretion of digestive enzymes and, in the case of pathogenic bacteria, release of toxins or other virulence factors. Indeed, methods to disrupt quorum sensing are currently being investigated as possible treatments for numerous diseases, including cystic fibrosis, epidemic cholera, and methicillin-resistant Staphylococcus aureus. In this paper we demonstrate the evolution of a quorum sensing behavior in populations of digital organisms. Specifically, we show that digital organisms are capable of evolving a strategy to collectively suppress self-replication, when the population density reaches a specific, evolved threshold. We present the evolved genome of an organism exhibiting this behavior and analyze the collective operation of this algorithm. Finally, through a set of experiments we demonstrate that the behavior scales to populations up to 400 times larger than those in which the behavior evolved.

Legged robots show promise for complex mobility tasks, such as navigating rough terrain, but the design of their control software is both challenging and laborious. Traditional evolutionary algorithms can produce these controllers, but require manual decomposition or other problem simplification because conventionally-used direct encodings have trouble taking advantage of a problem's regularities and symmetries. Such active intervention is time consuming, limits the range of potential solutions, and requires the user to possess a deep understanding of the problem's structure. This paper demonstrates that HyperNEAT, a new and promising generative encoding for evolving neural networks, can evolve quadruped gaits without an engineer manually decomposing the problem. Analyses suggest that HyperNEAT is successful because it employs a generative encoding that can more easily reuse phenotypic modules. It is also one of the first neuroevolutionary algorithms that exploits a problem's geometric symmetries, which may aid its performance. We compare HyperNEAT to FT-NEAT, a direct encoding control, and find that HyperNEAT is able to evolve impressive quadruped gaits and vastly outperforms FT-NEAT. Comparative analyses reveal that HyperNEAT individuals are more holistically affected by genetic operators, resulting in better leg coordination. Overall, the results suggest that HyperNEAT is a powerful algorithm for evolving control systems for complex, yet regular, devices, such as robots.