Improving evolutionary algorithms by MEANS of an adaptive parameter control approach

Evolutionary algorithms (EA) constitute a class of optimization methods that is widely used to solve complex scientific problems. However, EA often converge prematurely over suboptimal solutions, the evolution process is computational expensive, and setting the required EA parameters is quite difficult. We believe that the best way to address these problems is to begin by improving the parameter setting strategy, which will in turn improve the search path of the optimizer, and, we hope, ultimately help prevent premature convergence and relieve the computational burden. The strategy that will achieve this outcome, and the one we adopt in this research, is to ensure that the parameter setting approach takes into account the search path and attempts to drive it in the most advantageous direction. Our objective is therefore to develop an adaptive parameter setting approach capable of controlling all the EA parameters at once. To interpret the search path, we propose to incorporate the concept of exploration and exploitation into the feedback indicator. The first step is to review and study the available genotypic diversity measurements used to characterize the exploration of the optimizer over the search space. We do this by implementing a specifically designed benchmark, and propose three diversity requirements for evaluating the meaningfulness of those measures as population diversity estimators. Results show that none of the published formulations is, in fact, a qualified diversity descriptor. To remedy this, we introduce a new genotypic formulation here, the performance analysis of which shows that it produces better results overall, notwithstanding some serious defects. We initiate a similar study aimed at describing the role of exploitation in the search process, which is to indicate promising regions. However, since exploitation is mainly driven by the individuals’ fitness, we turn our attention toward phenotypic convergence measures. Again, the in-depth analysis reveals that none of the published phenotypic descriptors is capable of portraying the fitness distribution of a population. Consequently, a new phenotypic formulation is developed here, which shows perfect agreement with the expected population behavior. On the strength of these achievements, we devise an optimizer diagnostic tool based on the new genotypic and phenotypic formulations, and illustrate its value by comparing the impacts of various EA parameters. Although the main purpose of this development is to explore the relevance of using both a genotypic and a phenotypic measure to characterize the search process, our diagnostic tool proves to be one of the few tools available to practitioners for interpreting and customizing the way in which optimizers work over real-world problems. With the knowledge gained in our research, the objective of this thesis is finally met, with the proposal of a new adaptive parameter control approach. The system is based on a Bayesian network that enables all the EA parameters to be considered at once. To the authors’ knowledge, this is the first parameter setting proposal devised to do so. The genotypic and phenotypic measures developed are combined in the form of a credit assignment scheme for rewarding parameters by, among other things, promoting maximization of both exploration and exploitation. The proposed adaptive system is evaluated over a recognized benchmark (CEC’05) through the use of a steady-state genetic algorithm (SSGA), and then compared with seven other approaches, like FAUC-RMAB and G-CMA-ES, which are state-of-the-art adaptive methods. Overall, the results demonstrate statistically that the new proposal not only performs as well as G-CMA-ES, but outperforms almost all the other adaptive systems. Nonetheless, this investigation revealed that none of the methods tested is able to locate global optimum over complex multimodal problems. This led us to conclude that synergy and complementarity among the parameters involved is probably missing. Consequently, more research on these topics is advised, with a view to devising enhanced optimizers. We provide numerous recommendations for such research at the end of this thesis

Dynamic adversarial mining - effectively applying machine learning in adversarial non-stationary environments.

While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race between the system designer and the attackers. Any solution designed for such a domain needs to take into account an active adversary and needs to evolve over time, in the face of emerging threats. We term this as the ‘Dynamic Adversarial Mining’ problem, and the presented work provides the foundation for this new interdisciplinary area of research, at the crossroads of Machine Learning, Cybersecurity, and Streaming Data Mining. We start with a white hat analysis of the vulnerabilities of classification systems to exploratory attack. The proposed ‘Seed-Explore-Exploit’ framework provides characterization and modeling of attacks, ranging from simple random evasion attacks to sophisticated reverse engineering. It is observed that, even systems having prediction accuracy close to 100%, can be easily evaded with more than 90% precision. This evasion can be performed without any information about the underlying classifier, training dataset, or the domain of application. Attacks on machine learning systems cause the data to exhibit non stationarity (i.e., the training and the testing data have different distributions). It is necessary to detect these changes in distribution, called concept drift, as they could cause the prediction performance of the model to degrade over time. However, the detection cannot overly rely on labeled data to compute performance explicitly and monitor a drop, as labeling is expensive and time consuming, and at times may not be a possibility altogether. As such, we propose the ‘Margin Density Drift Detection (MD3)’ algorithm, which can reliably detect concept drift from unlabeled data only. MD3 provides high detection accuracy with a low false alarm rate, making it suitable for cybersecurity applications; where excessive false alarms are expensive and can lead to loss of trust in the warning system. Additionally, MD3 is designed as a classifier independent and streaming algorithm for usage in a variety of continuous never-ending learning systems. We then propose a ‘Dynamic Adversarial Mining’ based learning framework, for learning in non-stationary and adversarial environments, which provides ‘security by design’. The proposed ‘Predict-Detect’ classifier framework, aims to provide: robustness against attacks, ease of attack detection using unlabeled data, and swift recovery from attacks. Ideas of feature hiding and obfuscation of feature importance are proposed as strategies to enhance the learning framework\u27s security. Metrics for evaluating the dynamic security of a system and recover-ability after an attack are introduced to provide a practical way of measuring efficacy of dynamic security strategies. The framework is developed as a streaming data methodology, capable of continually functioning with limited supervision and effectively responding to adversarial dynamics. The developed ideas, methodology, algorithms, and experimental analysis, aim to provide a foundation for future work in the area of ‘Dynamic Adversarial Mining’, wherein a holistic approach to machine learning based security is motivated

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

Integration of Renewables in Power Systems by Multi-Energy System Interaction

This book focuses on the interaction between different energy vectors, that is, between electrical, thermal, gas, and transportation systems, with the purpose of optimizing the planning and operation of future energy systems. More and more renewable energy is integrated into the electrical system, and to optimize its usage and ensure that its full production can be hosted and utilized, the power system has to be controlled in a more flexible manner. In order not to overload the electrical distribution grids, the new large loads have to be controlled using demand response, perchance through a hierarchical control set-up where some controls are dependent on price signals from the spot and balancing markets. In addition, by performing local real-time control and coordination based on local voltage or system frequency measurements, the grid hosting limits are not violated

Ectropy of diversity measures for populations in Euclidean space

Measures to evaluate the diversity of a set of points (population) in Euclidean space play an important role in a variety of areas of science and engineering. Well-known measures are often used without a clear insight into their quality and many of them do not appropriately penalize populations with a few distant groups of collocated or closely located points. To the best of our knowledge, there is a lack of rigorous criteria to compare diversity measures and help select an appropriate one. In this work we define a mathematical notion of ectropy for classifying diversity measures in terms of the extent to which they tend to penalize point collocation, we investigate the advantages and disadvantages of several known measures and we propose some novel ones that exhibit a good ectropic behavior. In particular, we introduce a quasi-entropy measure based on a geometric covering problem, three measures based on discrepancy from uniform distribution and one based on Euclidean minimum spanning trees. All considered measures are tested and compared on a large set of random and structured populations. Special attention is also devoted to the complexity of computing the measures. Most of the novel measures compare favorably with the classical ones in terms of ectropy. The measure based on Euclidean minimum spanning trees turns out to be the most promising one in terms of the tradeoff between the ectropic behavior and the computational complexity