Evolutionary computing driven search based software testing and correction

For a given program, testing, locating the errors identified, and correcting those errors is a critical, yet expensive process. The field of Search Based Software Engineering (SBSE) addresses these phases by formulating them as search problems. This dissertation addresses these challenging problems through the use of two complimentary evolutionary computing based systems. The first one is the Fitness Guided Fault Localization (FGFL) system, which novelly uses a specification based fitness function to perform fault localization. The second is the Coevolutionary Automated Software Correction (CASC) system, which employs a variety of evolutionary computing techniques to perform testing, correction, and verification of software. In support of the real world application of these systems, a practitioner\u27s guide to fitness function design is provided. For the FGFL system, experimental results are presented that demonstrate the applicability of fitness guided fault localization to automate this important phase of software correction in general, and the potential of the FGFL system in particular. For the fitness function design guide, the performance of a guide generated fitness function is compared to that of an expert designed fitness function demonstrating the competitiveness of the guide generated fitness function. For the CASC system, results are presented that demonstrate the system\u27s abilities on a series of problems of both increasing size as well as number of bugs present. The system presented solutions more than 90% of the time for versions of the programs containing one or two bugs. Additionally, scalability results are presented for the CASC system that indicate that success rate linearly decreases with problem size and that the estimated convergence rate scales at worst linearly with problem size --Abstract, page ii

Polluting emissions standards and clean technology trajectories under competitive selection and supply chain pressure

Based on a model of industrial dynamics, this paper examines the impact of polluting emissions standards on trajectories of clean technologies implemented by firms subject to competitive selection and supply chain pressure. The model incorporates a few stylised facts on the relationships between environmental regulation, innovation and diffusion. The main objective is to highlight the forces influencing the long term dynamics of an industry faced with evolving emissions standards in a ‘history-friendly’ way. The paper gives guidance to the conditions of dynamic efficiency of emissions standards taking into account the coevolution of technology, user requirements and market structure. We show that emission standards not only play a significant role in orienting research and innovation activities of supplier firms, but they are also likely to support the diffusion of environmental innovation in the supply chain. In some cases, emission standards lead to prevent both a situation of lock-in on the supply side and a situation of behavioural inertia on the user side. Standards may thus lead to preserve a certain form of technological and behavioural diversity. Based on the computer simulations, it will be shown that the efficiency of standards depends on the nature of performance standards (process or product), on the market structure and on the timing of intervention.environmental innovation; industrial dynamics; environmental supply chain pressure; emission standards

Industry Life Cycle and the Evolution of an Industry Network

This paper addresses the problem of the general validity of models of the industry life cycle, which have been proposed to analyse the long-term evolution of many industries, exhibiting a typical pattern of shakeout. We study a case of non shake-out in the commercial jet aero-engine industry, marked by a small number of entry events distributed over 40 years of industry evolution, by no exits and by a resulting slowly increasing number of firms. We argue that the vertical structure of the industry, as represented by the network of vertical relations between aero-engine suppliers and aircraft manufacturers, "regulated" the process of entry and exit and posed the conditions for a non shakeout to take place.industry life cycle; entry, exit; network; vertical relations

Multiagent learning for locomotion and coordination in tensegrity robotics

Tensegrity structures are composed of pure compressional elements that are connected via a network of pure tensional elements. The concept of tensegrity promises numerous advantages to the field of robotics. Tensegrity robots are, however, notoriously difficult to control due to their oscillatory nature and nonlinear interaction between the components. Multiagent learning, a subtopic of artificial intelligence, provides the tools to address challenges of tensegrity robots. In multiagent learning, multiple entities simultaneously learn a task together while interacting with each other through the environment. This approach can be applied at two different levels: both to coordinate teams of multiple robots, and to control a single robot where different agents control different parts of the robot. In this work, we consider both cases, and apply two multiagent learning approaches (Reinforcement Learning and Evolutionary Algorithms) to tensegrity robotics problems at different levels. First, we take the model of an icosahedron robot, and use multiagent learning to control different parts. We use coevolutionary algorithms and fitness shaping to develop learning based robust rolling locomotion algorithm. After the locomotion aspect, we study multi-robot coordination using multiagent reinforcement learning and reward shaping methods. At this phase, we study reward shaping and develop methods to use reward shaping to improve the cooperation between multiple tensegrity robots. We explain how these results are simulated and validated by using physical tensegrity robots. Last, we explain how these results helped design and development of a tensegrity robot with rolling capability: SUPERBall

Cyber security research frameworks for coevolutionary network defense

Cyber security is increasingly a challenge for organizations everywhere. Defense systems that require less expert knowledge and can adapt quickly to threats are strongly needed to combat the rise of cyber attacks. Computational intelligence techniques can be used to rapidly explore potential solutions while searching in a way that is unaffected by human bias. Several architectures have been created for developing and testing systems used in network security, but most are meant to provide a platform for running cyber security experiments as opposed to automating experiment processes. In the first paper, we propose a framework termed Distributed Cyber Security Automation Framework for Experiments (DCAFE) that enables experiment automation and control in a distributed environment. Predictive analysis of adversaries is another thorny issue in cyber security. Game theory can be used to mathematically analyze adversary models, but its scalability limitations restrict its use. Computational game theory allows us to scale classical game theory to larger, more complex systems. In the second paper, we propose a framework termed Coevolutionary Agent-based Network Defense Lightweight Event System (CANDLES) that can coevolve attacker and defender agent strategies and capabilities and evaluate potential solutions with a custom network defense simulation. The third paper is a continuation of the CANDLES project in which we rewrote key parts of the framework. Attackers and defenders have been redesigned to evolve pure strategy, and a new network security simulation is devised which specifies network architecture and adds a temporal aspect. We also add a hill climber algorithm to evaluate the search space and justify the use of a coevolutionary algorithm --Abstract, page iv

A meta-architecture analysis for a coevolved system-of-systems

Modern engineered systems are becoming increasingly complex. This is driven in part by an increase in the use of systems-of-systems and network-centric concepts to improve system performance. The growth of systems-of-systems allows stakeholders to achieve improved performance, but also presents new challenges due to increased complexity. These challenges include managing the integration of asynchronously developed systems and assessing SoS performance in uncertain environments. Many modern systems-of-systems must adapt to operating environment changes to maintain or improve performance. Coevolution is the result of the system and the environment adapting to changes in each other to obtain a performance advantage. The complexity that engineered systems-of-systems exhibit poses challenges to traditional systems engineering approaches. Systems engineers are presented with the problem of understanding how these systems can be designed or adapted given these challenges. Understanding how the environment influences system-of-systems performance allows systems engineers to target the right set of capabilities when adapting the system for improved performance. This research explores coevolution in a counter-trafficking system-of-systems and develops an approach to demonstrate its impacts. The approach implements a trade study using swing weights to demonstrate the influence of coevolution on stakeholder value, develops a novel future architecture to address degraded capabilities, and demonstrates the impact of the environment on system performance using simulation. The results provide systems engineers with a way to assess the impacts of coevolution on the system-of-systems, identify those capabilities most affected, and explore alternative meta-architectures to improve system-of-systems performance in new environments --Abstract, page iii

Genetic Improvement of Software: a Comprehensive Survey

Genetic improvement (GI) uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of GI back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. GI has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between GI and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields

Complex and Adaptive Dynamical Systems: A Primer

An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems. It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.Comment: unformatted version of the textbook; published in Springer, Complexity Series (2008, second edition 2010