The Graph Evolution Tool (GET)was built to generate personal contact networks representing who can infect whom within a community. The tool is expanded in order to permit an infection scheme which divides the community into different districts, thus permitting within-district and between-district infections. The evolutionary algorithm comprising GET is expanded upon to simulate communities which include 512 individuals in up to eight districts, initially infecting one person in one district and spreading through a community. The overall goal is to generate communities that will maximize the length of an epidemic. The problem associated with adequately exploring the numerous parameters accompanying evolutionary algorithms is addressed using a point packing and insight from previous work. The Susceptible-Infected-Removed (SIR)model of infection was chosen as it provides a sufficient balance of simplicity and complexity for the problem.

In this study, deep learning will be used to test the predictability of stock trends. Stock markets are known to be volatile, prices fluctuate, and there are many complicated financial indicators involved. Various data including news or financial indicators can be used to predict stock prices. In this study, the focus will be on using past stock prices and using technical indicators to increase the performance of the results. The goal of this study is to measure the accuracy of predictions and evaluate the results. Historical data is gathered for Apple, Microsoft, Google and Intel stocks. A prediction model is created by using past data and technical indicators were used as features in the model. The experiments were performed by using long short-term memory networks. Different approaches and techniques were tested to boost the performance of the results. To prove the usability of the final model in the real world and measure the profitability of results backtesting was performed. The final results show that while it is not possible to predict the exact price of a stock in the future to gain profitable results, deep learning can be used to predict the trend of stock markets to generate buy and sell signals.

Parkinson's disease (PD) is a degenerative disorder of the central nervous system that has many debilitating symptoms which affect the patient's motor system and can cause significant changes in their gait. By using genetic programming, we aim to develop descriptive symbolic nonlinear models of PD patient gait from time series data recorded from pressure sensors under subjects' feet. When compared to popular types of linear regression (OLS and LASSO), the nonlinear models fit their data better and generalize to unseen data significantly better. It was found that models developed for healthy control subjects generalized to other control subjects well, however the models trained on subjects with PD did not generalize well to other PD patients, which complicates the issue of being able to detect the progression of the disease. It is suspected that health care professionals can have difficulty classifying PD due to a lack of accurate data from patient reports; having individually trained models for active monitoring of patients would help in effectively diagnosing PD.

Cities, while exciting in their visualization and permitting several layouts, do not take into account the placement of crucial characters which might be part of the narrative. Narrative graphs, a connected graph of all potential and existing relations within a game, can enable an ability to find a Nonplayer Character (NPC) who is likely to live nearby, under the assumption that those who interact most frequently are also close in distance. We examine the use of an evolutionary graph compression method and a method using simulated diffusion to cluster features based on relational information about players to generate relationally intimate groups. This clustering can be used to generate information about the game world and cities to inform PCG as to how the connectivity of these areas is, and should be, arranged. The algorithms are validated as being human competitive.

Epidemic contact tracing examines the movement of infection through a population based upon links in a contact network, and weighted networks represent the potential of transfer of the contagion. Graph compression reduces the size of a network by merging groups of nodes into supernodes. This study considers the use of genetic algorithms to select the nodes to be merged, grouping together highly connected sections of the graphs. Examined is a dataset that is extracted from contacts that occurred during several days of the "Infectious: Stay Away" event. The incorporation of weights, to indicate the strength of interactions between individuals, is an important contribution of this work. The demonstrated outcomes are that by including weighted information on the edges, there is more effective detection of highly interacting subgroups when compared to the unweighted version of graphs. These methods not only compress the networks with a low rate of distortion, but also the identification of supernodes in the networks allows for better targeting of interventions by public health upon individuals in such groups. This is crucial because when one member becomes infected, all members of the group are exposed to the contagion.

The development of general edit metric decoders is a challenging problem, especially with the inclusion of additional biological restrictions that can occur when using error correcting codes in biological applications. Side effect machines (SEMs), an extension of finite state machines, can provide efficient decoding algorithms for such edit metric codes.Several codes of varying lengths are used to study the effectiveness of evolutionary programming (EP) as a general approach for finding SEMs for edit metric decoding. Direct and fuzzy classification methods are compared while also changing some of the EP settings to observe how decoding accuracy is affected. Regardless of code length, the best results are found using the fuzzy classification methods. For codes of length 10, a maximum accuracy of up to 99.4% is achieved for distance 1 whereas distance 2 and 3 achieve up to 97.1% and 85.9%, respectively. The accuracy suffers for longer codes, as the maximum accuracies achieved by codes of length 14 were 92.4%, 85.7% and 69.2% for distance 1, 2, and 3 respectively. Additionally, the SEMs are examined for potential bloat by comparing the number of reachable states against the total number of states. Bloat is seen more in larger machines than it is in smaller machines. Furthermore, the results are analyzed to find potential trends and relationships among the parameters, with the most consistent trend being that, when allowed, the longer codes generally show a propensity for larger machines.

Public-key cryptography is a fundamental compo- nent of modern electronic communication that can be constructed with many different mathematical processes. Presently, cryp- tosystems based on elliptic curves are becoming popular due to strong cryptographic strength per small key size. At the heart of these schemes is the intractability of the elliptic curve discrete logarithm problem (ECDLP). Pollard’s Rho algorithm is a well known method for solving the ECDLP and thereby breaking ciphers based on elliptic curves. It has the same time complexity as other known methods but is advantageous due to smaller memory requirements. This paper considers how to speed up the Rho process by modifying a key component: the iterating function, which is the part of the algorithm responsible for determining what point is considered next when looking for a collision. It is replaced with an alternative that is found through an evolutionary process. This alternative consistently and significantly decreases the number of iterations required by Pollard’s Rho Algorithm to successfully find a solution to the ECDLP.

Parkinson's Disease is a disorder with diagnostic symptoms that include a change to a walking gait. The disease is problematic to diagnose. An objective method of monitoring the gait of a patient is required to ensure the effectiveness of diagnosis and treatments. We examine the suitability of Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) Models compared to Symbolic Regression (SR) using genetic programming that was demonstrated to be successful in previous works on gait. The XGBoost and ANN models are found to out-perform SR, but the SR model is more human explainable.

DNA Fragment assembly – an NP-Hard problem – is one of the major steps in of DNA sequencing. Multiple strategies have been used for this problem, including greedy graph-based algorithms, deBruijn graphs, and the overlap-layout-consensus approach. This study focuses on the overlap-layout-consensus approach. Heuristics and computational intelligence methods are combined to exploit their respective benefits. These algorithm combinations were able to produce high quality results surpassing the best results obtained by a number of competitive algorithms specially designed and tuned for this problem on thirteen of sixteen popular benchmarks. This work also reinforces the necessity of using multiple search strategies as it is clearly observed that algorithm performance is dependent on problem instance; without a deeper look into many searches, top solutions could be missed entirely.

A key aspect of multi-energy microgrids (MEMGs) is the capability to efficiently convert and store energy in order to reduce the costs and environmental impact. Peer-to-peer (P2P) energy trading is a novel paradigm for decentralized energy market designs. In this paper, we investigate the external P2P energy trading problem and internal energy conversion problem within interconnected residential, commercial and industrial MEMGs. These two problems are complex decision-making problems with enormous high-dimensional data and uncertainty, so a multi-agent deep reinforcement learning approach combining the multi-agent actor-critic algorithm with the twin delayed deep deterministic policy gradient algorithm is proposed. The proposed approach can handle the high-dimensional continuous action space and aligns with the nature of P2P energy trading with multiple MEMGs. Simulation results based on three real-world MG datasets show that the proposed approach significantly reduces each MG's average hourly operation cost. The impact of carbon tax pricing is also considered.