Self-driving automata (SDAs) are extensions of finite state automata that both read and output symbols. Previously, genetic algorithms were used to evolve SDAs to generate sequences that closely matched given DNA sequences, with the eventual goal of finding patterns in those sequences that were not achievable using traditional biological methods. Previous work demonstrated that improvements in fitness were almost exclusively due to mutation not crossover. This paper evaluates the use of evolutionary programming (EP) using SDAs as the representation. EP allows for easy handling of multiple types of mutation, including changes in the number of states, which was not available in earlier approaches. This work uses three fitness metrics: primary sequence matching fitness, secondary sequence similarity fitness, and a relative fitness function known as bout score. Tested on a set of six target DNA sequences, the approach matched 84.4–98.2% of each sequence, and discovered some features of the sequences for future exploration.

A population of Self-Driving Automata (SDAs) is evolved using a steady-state evolutionary algorithm tasked with matching six DNA sequences. This is a step towards using SDAs to assist in identifying patterns within groups of DNA sequences for which conventional methods for identifying patterns fail. The fitness function uses both a primary and a secondary fitness metric to determine the overall fitness of an SDA. The primary metric is sequence matching fitness, evaluating how well the evolved sequence matches the target sequence. The secondary metric is sequence diversity fitness: considering pairs of sequences with the same primary fitness, this counts the number of differences that exist between them. It is found that promoting diversity in this manner by using a secondary fitness metric dramatically improves results for the primary fitness metric.

An evolutionary algorithm is used to evolve a population of self-driving automata (SDAs), modified state machines, that are used to produce output in the form of a DNA sequence. The SDAs are evaluated based on their ability to create sequences that closely match all DNA sequences within a given set. This is evaluated in a pairwise fashion, but attempts to match all sequences concurrently, using two fitness functions that are differentiated by whether they allow for gaps. Additionally, a secondary fitness metric, Sequence Diversity Fitness, encourages diversity among the output of the SDAs within the population throughout evolution. The target sequences are Φ-segments of dehydrin proteins, which are weakly-conserved, can vary considerably in length, and for which traditional methods fail when used to find patterns within them. The ultimate goal is to use SDAs to assist in identifying patterns within Φ-segments. Locating such a pattern could prove fruitful for understanding the functions of dehydrins and how they contribute to the protection of plants from abiotic stresses. Several sets of target sequences are used for analysis, with some sets being more closely-related than others. The evolutionary algorithm was found to produce sequences that matched (according to one of the fitness functions) up to 100% of a given set of target sequences under certain conditions, with closely-related sequences being more accurately matched.

Parkinson’s disease is an extremely debilitating condition where the brain is not producing enough dopamine to accurately coordinate movement. One symptom of Parkinson’s disease, freezing of gait, prevents the affected person from either starting to walk or continuing walking. We use a dataset that provides time-series data of volunteers’ gait while performing four different tasks of varying complexity. Symbolic regression through genetic programming is applied to the dataset to create models of the gait of volunteers with and without Parkinson’s disease, including those who may be experiencing freezing of gait, while also factoring in their medication status (ON or OFF). Comparing the gait of volunteers in the different groups, the methodology produced models that were most accurate for the group of PD patients with freezing of gait when off their medication applied to other volunteers in the same group. It was also found that for some volunteers it was not possible to distinguish between ON and OFF medication states, providing a possible indication of issues with their medication.

An evolutionary algorithm is employed to evolve contact networks representing interactions between individuals in a population. These networks play a crucial role in providing insights into epidemic behaviour and how viruses propagate through the population. The networks are evolved using two fitness functions: one focused on maximizing infection severity and one focused on maximizing the total number of infections (spread). Both evaluate potential networks by simulating SIR epidemics in the context of epidemic variants. The impact of different types of immunity and different probabilities of new variants being generated are evaluated with respect to the number and severity of infections, the characteristics of the evolved contact networks, and the length of the epidemic. The evolutionary algorithm successfully created networks likely to result in more severe infections when focusing on epidemic severity and networks likely to result in a higher number of infections of any severity when focusing on spread, although the immunity type and variant probability both had significant impact.

Two evolutionary algorithms are presented for the construction of weighted graphs: one based on self-driving automata (SDA), and one based on “editing” the edges of a graph. The algorithms are evaluated for their success at generating weighted contact networks likely to exhibit specified epidemic behaviour. Two main problems are considered: maximizing the length of the epidemic, and matching the profile (“curve”) of an epidemic, including one based on real-life data. Both algorithms significantly improve upon previous results using unweighted graphs. In most experiments the best overall results are obtained by the SDA algorithm, while the edge-editing algorithm usually has better mean fitness. This result is in part due to the SDA algorithm being more exploratory when compared to the one based on edge editing, which is more exploitative.

Evolutionary algorithms are used to evolve Self-Driving Automata (SDAs), finite automata that both read and output symbols. The output of the SDA can be used to generate biological data in the form of sequences or networks. The fitness of an SDA is assessed based on its ability to match real target data: DNA sequences and weighted contact networks. In sequence matching, the SDA method achieves 96.5% accuracy for one of the sequences, and over 90% for half of the target sequences, which range in length from 57 to 102 bases. In network matching, the SDA method is compared to another well-known method using three fitness functions. While the SDA method successfully reproduces multiple clusters in the target network, in general the results lag behind the comparator method. Several avenues for future work are identified, with the eventual goal of using SDAs to identify patterns in biological sequence data.

Personal contact networks are used to represent the social connections that exist between individuals within a population. Producing accurate networks that represent the actual vectors of infection that exist within a network can be useful for modelling epidemic trajectory and outcomes, which is significantly impacted by a network's structure. An evolutionary algorithm is used to evolve these networks subject to two fitness measures: epidemic duration and epidemic spread through a population. With each infection there is a small probability of a new variant being generated. Being infected with one variant provides partial immunity to future variants. This allows us to evaluate the impact of each variant, a significant innovation in comparison to other work. The amount by which each variant was allowed to change had a significant impact upon epidemic spread. For epidemic duration, the probability of new variants was the primary cause of increased epidemic duration.

A generative evolutionary algorithm is used to evolve weighted personal contact networks that represent physical contact between individuals, and thus possible paths of infection during an epidemic. The evolutionary algorithm evolves a list of edge-editing operations applied to an initial graph. Two initial graphs are considered, a ring graph and a power-law graph. Different probabilities of infection and a wide range of weights are considered, which improve performance over other work. Modified edge operations are introduced, which also improve performance. It is shown that when trying to maximize epidemic duration, the best results are obtained when using the ring graph as the initial graph. When attempting to match a given epidemic profile, similar results are obtained when using either initial graph, but both improve performance over other work.

Parkinson's disease (PD), a neurodegenerative disease with symptoms hard to distinguish from other disorders, affects millions of people globally. Among the symptoms of PD, changes in gait have been used as a primary diagnosis factor. Symptoms of PD include bradykinesia, tremors, depression, hallucinations, cognitive decline, and falls. This study presents a dataset that records data on PD patients who experience freezing of gait, including data for medication in the “on” and “off” states. Classification is applied to two problems relating to this PD data: first, to distinguish PD patients from healthy individuals, and second, to determine effectiveness of medication for freezing of gait. Among the classifiers considered, Multilayer Perceptron, K-Nearest Neighbors, Random Forest, and Support Vector Machine obtain the best results when applied to these problems.

An evolutionary algorithm is used to evolve personal contact networks representing the individuals in a population and the interactions between them. Such networks can be used to track the progress of an epidemic, as it passes from infected individuals to others. Two fitness functions are used: epidemic duration and epidemic spread. Each of these is evaluated in the context of new variants being introduced during the course of the epidemic. Individuals infected with one variant obtain immunity to that variant and possible partial immunity to future variants. Two frameworks for epidemic variants are presented. In the first, infectivity is coupled directly to how well an individual's immunity covers the variant. In the second, infectivity is decoupled, causing a much higher number of infections but with many of lessened severity due to immunity.

In this paper we evaluate the impact of different lockdown strategies upon the total number of infections during an epidemic. The strategies are based upon the percentage of the population infected during a given time step, as well as upon the amount by which interactions must be reduced during lockdown. We use a weighted personal contact network to represent the population, its interactions, and the relative strengths of those interactions. During lockdown edges from this network are removed. We use an evolutionary algorithm to choose the set of edges to be removed so as to minimize infections, comparing different strategies. We show that allowing the evolutionary algorithm to choose which edges to remove significantly reduces the overall number of infections in comparison to random selection. In fact, the EA results for the least stringent conditions were similar or better to the random results for the most stringent conditions, showing that a judicious choice of restrictions during lockdown has the greatest effect on reducing infections. The evolutionary algorithm tends to favour a situation in which during lockdown individuals would reduce their number of contacts, as opposed to lessening the strength of their connections.

A generative evolutionary algorithm is used to create personal contact networks representing which individuals can infect others during an infectious disease scenario. Two problems are considered: (i) finding networks that maximize the length of a simulated epidemic, and (ii) finding networks that match given epidemic profiles. A significant innovation is the introduction of weighted edges to represent the strength of the contact between individuals. Different weight initialization conditions are investigated and evaluated for their performance using a parameter selection mechanism designed to explore the parameter space. Results show that weighted edges were able to increase the overall performance achieved by the evolved networks for both problems considered. Furthermore, it is shown that initializing the weights with a value greater than one further improves performance. The results of the parameter selection mechanism were used to test additional parameter settings thoroughly which further maximize the length of the simulated epidemic for the evolved graphs.

Contact networks are used as a representation for the modeling of illness transmission. In this study, we represent not only the links of the transmissions but also utilize a weighted graph to represent the probability of transfer. These graphs can be large and complex when taking into account the number of contacts used in tracing. Compression of the graph allows for the development of community detection as well as providing a simpler graph. By examining the contact networks developed by an evolutionary algorithm for compression, it is discovered that the choice of fitness function and the appropriate weighting of edges leads to a different compressed graph, finding different connected communities; this is also true when compared to the communities identified by the Louvain community detection algorithm. This demonstrates the importance of considering weighting in contact networks, and suggests that in the future an understanding of the community structure should be utilized by public health officials.

How best to apply vaccines to a population is an open problem. It is trivial to derive intuitive strategies, but until tested, their efficacy is not known. This problem is particularly challenging when considering the dynamics of social contact networks and their changes over time. A system for automatically discovering tested vaccination strategies with evolutionary computation has been improved upon to include additional graph metrics and to generate vaccination strategies for dynamic graphs, something that is expected of real social networks within communities. The system's ability to generate effective strategies was demonstrated along with a comparison of the strategies developed when fit to a static graph versus a dynamic graph. It was observed that the additional computational resources required to generate strategies on a dynamic graph may not be necessary as strategies developed for static graphs performed similarly well; however, the authors are careful to acknowledge that results may differ significantly when adjusting the systems many parameters.

This study introduces a new game that models competition in foraging behavior. Two moose decide, in each time period, which of three foraging areas to visit. Moose in the same foraging area fight, gaining no forage and also damaging some forage during their conflict. Moose alone in a foraging area eat, with the forage in each field being replenished with a logistic growth model. This creates a relatively complex game with a rich strategy space in which the moose try to maximize their forage intake. The game is a coordination game, as the moose try to avoid conflict which does not maximize forage intake. The paper reports the results of two student competitions at Innopolis University and performs agent evolution to verify the existence of a rich strategy space for the game.

A multi-objective genetic algorithm is applied to the problem of identifying genes associated with Alzheimer's disease. The input to the genetic algorithm is a set of centrality measures obtained by merging various biological evidence types into a complex network, based on a set of 11 genes already known to be associated with this disease. In terms of leave-one-out validation, the strongest results are obtained using betweenness, with ranking showing that better results are sometimes obtained by including either stress or load with betweenness. The overall ranking of the genes across all runs is examined and suggests some genes worthy of further study with respect to their link to this disease. The methodology is also evaluated with respect to robustness by modifying the original network by a range of percentages, and applying the methodology to these variations. The results show that the methodology returns very similar results under these circumstances.

The dropping cost of sequencing human DNA has allowed for fast development of several projects around the world generating huge amounts of DNA sequencing data. This deluge of data has run up against limited storage space, a problem that researchers are trying to solve through compression techniques. In this study we address the compression of SAM files, the standard output files for DNA alignment. We specifically study lossy compression techniques used for quality values reported in the SAM file and analyze the impact of such lossy techniques on the CRAM format. We present a series of experiments using a data set corresponding to individual NA12878 with three different fold coverages. We introduce a new lossy model, dynamic binning, and compare its performance to other lossy techniques, namely Illumina binning, LEON and QVZ. We analyze the compression ratio when using CRAM and also study the impact of the lossy techniques on SNP calling. Our results show that lossy techniques allow a better CRAM compression ratio. Furthermore, we show that SNP calling performance is not negatively affected and may even be boosted.

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.

Personal contact networks that represent social interactions can be used to identify who can infect whom during the spread of an epidemic. The structure of a personal contact network has great impact upon both epidemic duration and the total number of infected individuals. A vaccine, with varying degrees of success, can reduce both the length and spread of an epidemic, but in the case of a limited supply of vaccine a vaccination strategy must be chosen, and this has a significant effect on epidemic behaviour.In this study we consider four different vaccination strategies and compare their effects upon epidemic duration and spread. These are random vaccination, high degree vaccination, ring vaccination, and the base case of no vaccination. All vaccinations are applied as the epidemic progresses, as opposed to in advance. The strategies are initially applied to static personal contact networks that are known ahead of time. They are then applied to personal contact networks that are evolved as the vaccination strategy is applied. When any form of vaccination is applied, all strategies reduce both duration and spread of the epidemic. When applied to a static network, random vaccination performs poorly in terms of reducing epidemic duration in comparison to strategies that take into account connectivity of the network. However, it performs surprisingly well when applied on the evolved networks, possibly because the evolutionary algorithm is unable to take advantage of a fixed strategy.

Parkinson's disease is a neurodegenerative disease that affects close to 10 million with various symptoms including tremors and changes in gait. Observing differences or changes in an individual's manifestations of gait may provide a mechanism to identify Parkinson's disease and understand specific changes. In this study, timeseries data from both Control subjects and Parkinson's disease patients was modelled with symbolic regression and extreme gradient boosting. Model effectiveness was analyzed along with the differences in the models between modelling strategies, between Control subjects and Parkinson's disease patients, and between normal walking and walking while under a cognitive load. Both modelling strategies were found to effective. The symbolic regression models were more easily interpreted, while extreme gradient boosting had higher overall accuracy. Interpretation of the models identified certain characteristics that distinguished Control subjects from Parkinson's disease patients and normal walking conditions from walking while under a cognitive load.

In recent years, researchers have been exploring alternative methods to solving Integer Prime Factorization, the decomposition of an integer into its prime factors. This has direct application to cryptanalysis of RSA, as one means of breaking such a cryptosystem requires factorization of a large number that is the product of two prime numbers. This paper applies three different genetic algorithms to solve this issue, utilizing mathematical knowledge concerning distribution of primes to improve the algorithms. The best of the three genetic algorithms has a chromosome that represents m in the equation prime = 6 m ± 1, and is able to factor a number of up to 22 decimal digits. This is a significantly larger number than the largest factored by comparable methods in earlier work. This leads to the conclusion that approaches such as genetic algorithms are a promising avenue of research into the problem of integer factorization.

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.

Evolutionary algorithms are used to generate personal contact networks, modelling human populations, that are most likely to match a given epidemic profile. The Susceptible-Infected-Removed (SIR) model is used and also expanded upon to allow for an extended period of infection, termed the SIIR model. The networks generated for each of these models are thoroughly evaluated for their ability to match nine different epidemic profiles. The addition of the SIIR model showed that the model of infection has an impact on the networks generated. For the SIR and SIIR models, these differences were relatively minor in most cases.

It is important to understand how best to apply a limited number of vaccines to a population such that the spread of a disease, like SARS-CoV-2, is minimized. Although intuition provides a number of mitigation strategies that may be effective, they remain largely untested.A system was developed to test a given disease mitigation strategy. It is designed to work with a graph representing a real social network. A Genetic Programming system was used to discover novel mitigation strategies that are easily interpretable by a public health decision maker.Effective strategies were developed by the GP system. The strategies are easily explainable and intuitive. Novel mitigation strategies were compared to simple baseline strategies with varying success using a number of different metrics. Many of these strategies proved effective in general, however the topology of the graph influences the effectiveness of a strategy.The system has been made publicly available and the authors call on the research community to contribute their own mitigation strategies and measure their efficacy.

Traditionally, models developed to estimate resting energy expenditure (REE) in the bariatric population have been limited to linear modelling based on data from `normal' or `overweight' individuals - not `obese'. This type of modelling can be restrictive and yield functions which poorly estimate this important physiological outcome.Linear and nonlinear models of REE for individuals after bariatric surgery are developed with linear regression and symbolic regression via genetic programming. Features not traditionally used in REE modelling were also incorporated and analyzed and genetic programming's intrinsic feature selection was used as a measure of feature importance.A collection of effective new linear and nonlinear models were generated. The linear models generated outperformed the nonlinear on testing data, although the nonlinear models fit the training data better. Ultimately, the newly developed linear models showed an improvement over existing models and the feature importance analysis suggested that the typically used features (age, weight, and height) were the most important.

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.

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.

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.

Creating a representation capable of generating personal contact networks that are most likely to exhibit specific epidemic behavior is difficult due to the inherit volatility of an epidemic and the numerous parameters accompanying the problem. To surpass these hurdles, evolutionary algorithms are used to create a generative solution which generates personal contact networks, modeling human populations, to satisfy the epidemic duration and epidemic profile matching problems. This representation is entitled the Local THADS-N representation. Two new operators are added to the original THADS-N system, and tested with a traditional parameter sweep and a parameter selection method known as point packing on nine epidemic profiles. Additionally, a new epidemic model is implemented in order to allow for lost immunity within a population thus increasing the length of an epidemic.

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.

Network graphs appear in a number of important biological data problems, recording information relating to protein-protein interactions, gene regulation, transcription regulation and much more. These graphs are of such a significant size that they are impossible for a human to understand. Furthermore, the ever-expanding quantity of such information means that there are storage issues. To help address these issues, it is common for applications to compress nodes to form supernodes of similarly connected components. In previous graph compression studies it was noted that such supernodes often contain points from disparate parts of the graph. This study aims to correct this flaw by only allowing merges to occur within a local neighbourhood rather than across the entire graph. This restriction was found to not only produce more meaningful compressions, but also to reduce the overall distortion created by the compression for two out of three biological networks studied.

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.

The accurate modeling of epidemics on social contact networks is difficult due to the variation between different epidemics and the large number of parameters inherent to the problem. To reduce complexity, evolutionary computation is used to create a generative representation of the epidemic model. Previous gains from the use of local, verses global, operators are further explored to better balance exploration and exploitation of the genetic algorithm. A typical parameter study is conducted to test this new local operator and the new method of point packing is utilized as a proof of concept to perform a better search of the parameter space. All experiments from both approaches are tested against nine epidemic profiles. The point-packing driven parameter search demonstrates that the algorithm parameters interact substantially and in a non-linear fashion, and also shows that the good parameter settings are problem specific.

Side Effect Machines (SEMs) are an extension of finite state machines which place a counter on each node that is incremented when that node is visited. Previous studies examined a genetic algorithm to discover node connections in SEMs for edit metric decoding for biological applications, namely to handle sequencing errors. Edit metric codes, while useful for decoding such biologically created errors, have a structure which significantly differentiates them from other codes based on Hamming distance. Further, the inclusion of biologically- motivated restrictions on allowed words makes development of decoders a bespoke process based on the exact code used. This study examines the use of evolutionary programming for the creation of such decoders, thus allowing for the number of states to be evolved directly, not witnessed in previous approaches which used genetic algorithms. Both direct and fuzzy decoding are used, obtaining correct decoding rates of up to 95% in some SEMs.

Alzheimer's disease (AD) is an irreversible, progressive neurological disorder that causes memory and thinking skill loss. Many different methods and algorithms have been applied to extract patterns from neuroimaging data in order to distinguish different stages of Alzheimer's disease (AD). However, the similarity of the brain patterns in older adults and in different stages makes the classification of different stages a challenge for researchers. In this paper, convolutional neuronal network architecture AlexNet was applied to fMRI datasets to classify different stages of the disease. We classified five different stages of Alzheimer's using a deep learning algorithm. The method successfully classified normal healthy control (NC), significant memory concern (SMC), early mild cognitive impair (EMCI), late cognitive mild impair (LMCI), and Alzheimer's disease (AD). The model was implemented using GPU high performance computing. Before applying any classification, the fMRI data were strictly preprocessed. Then, low to high level features were extracted and learned using the AlexNet model. Our experiments show significant improvement in classification. The average accuracy of the model was 97.63%. We then tested our model on test datasets to evaluate the accuracy of the model per class, obtaining an accuracy of 94.97% for AD, 95.64% for EMCI, 95.89% for LMCI, 98.34% for NC, and 94.55% for SMC.

Hierarchical clustering via neighbor joining, widely used in biology, can be quite sensitive to the addition or deletion of single taxa. In an earlier study it was found that neighbor joining trees on random data were commonly quite unstable in the sense that large re-arrangements of the tree occurred when the tree was reconstructed after the deletion of a single data point. In this study, we use an evolutionary algorithm to evolve extremely stable and unstable data sets for a standard neighbor-joining algorithm and then check the stability using a novel type of clustering called bubble clustering. Bubble clustering is an instance of associator clustering. The stability measure used is based on the size of the subtree containing each pair of taxa, a quantity that provides an objective measure of a given trees hypothesis about the relatedness of taxa. It is shown experimentally that even in data sets evolved to be stable for a standard neighbor joining algorithm, bubble clustering is a significantly more stable algorithm.

Public-key cryptography is a fundamental component of modern electronic communication that can be constructed with many different mathematical processes. Presently, cryptosystems 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.

Embeddable biomarkers are short strands of DNA that can be incorporated into genetic constructs to enable later identification. They are drawn from error correcting codes on the DNA alphabet relative to the Levenshtein metric. This study uses three types of evolutionary algorithms to improve the best known size of DNA error correcting codes, improving the bound for nine different code parameters. One of the algorithms is used on only one set of code parameters, correcting an oversight in an earlier study. The other two algorithms are a ring optimizer and a hybridizing evolutionary algorithm that exploits previously known codes. The ring optimizer improves two code size bounds and sets the stage for the hybridizer to improve four more. The hybridizer requires the results of a previous search as a starting point. Starting with known codes from earlier work, it improves a total of six bounds. The best results found by this algorithm used the results of the ring optimizer as a starting point. The paper discusses the issue of building a suite of cooperative code-search algorithms as a good target for future work.

Storage and processing of biological networks is challenging and costly due to the large sizes of many of these networks. Compression of such graphs is one possible solution to this problem. This study presents two single-objective genetic algorithms, along with one multi-objective algorithm, to address the problem of graph compression. The fitness functions were both based on the concept of merging nodes based on “similarity” but each defined that similarity in a different way. The multiobjective GA based on NSGA-II worked to find a balance between the compression ratio and the similarity. The methods were applied to three different biological networks with different characteristics. The single-objective GAs were first applied to these networks for a fixed compression ratio. Then based on the results of the multiobjective GA, target compression ratios were chosen for each graph and the single-objective GAs were applied to this target. Applying the single-objective GAs to a target identified in this manner was significantly more successful than using the results from the multiobjective GA for the same compression ratio.

The salmon algorithm is a metaheuristic inspired by the behaviour of salmon swimming upstream to spawn. It has previously shown success when used for the creation of sets of robust tags for DNA sequencing applications, as well as for the travelling salesman problem. In this paper the salmon algorithm is evaluated for the construction of optimal covering and error-correcting codes, which are related to sequencing applications, as well as for the DNA fragment assembly problem, which is related to the travelling salesman problem. Parameter tuning for the salmon algorithm is extensively studied, as well as the use of automated parameter tuning.

Disease-gene association attempts to determine which genes are involved with genetic diseases. Various methodologies have been applied to this problem for different diseases. In earlier work, two evolutionary approaches were used to analyze the complex network of gene interaction. This paper presents an improvement upon the genetic programming approach using a variety of centrality measures to analyze the networks. This approach is applied to both Parkinson's disease and breast cancer.

Many real-world graphs, including those storing various forms of biological data, are of such large size that storing and processing their information has too high a cost. As a result, one possible solution is to compress the graphs by merging nodes into supernodes. This study introduces a genetic algorithm for graph compression that is based on the similarity of nodes, where two nodes are considered similar if a high proportion of their neighbours are in common. The methodology was applied to three real-world graphs storing widely varying data, as well as the gene regulatory network of E. coli. This study used a fixed compression rate of 25% as a target for the graphs. Results for a parameter study of variation operators exhibit a strong preference for crossover, in comparison to mutation which was found to be disruptive to graph structure.

Embeddable biomarkers are short strands of DNA that can be incorporated into genetic constructs to enable later identification. They are drawn from error correcting codes on the DNA alphabet relative to the Levenshtein metric. This study revisits the Conway variation operator which can serve as a population initializer, mutation operator, or crossover operator depending on its mode of application. The algorithm is applied to a part of the space of code parameters where it had not previously been tested. A parameter setting study establishes that an evolutionary algorithm using this variation operator requires a small population and an intermediate rate of introduction of new material (mutation). Better values, sometimes more than quadrupling previous code sizes, are found for eighteen different code parameters with relatively large word size and high error correction ability. The table of best known code sizes is updated by this study. The parameter study performs comparison with the novel total maximum fitness statistic and a technique for displaying time of last innovation within evolutionary algorithms is introduced.

Disease-gene association attempts to understand the relationship between genetic diseases and the genes associated with them. Many genetic diseases are not due to defects in a single gene, but rather are a result of various genetic components interacting in a complex network. We examine the use of two evolutionary computation approaches for disease-gene association, both using notions related to complex networks. When applied to the problem of identifying genes involved in Parkinson's Disease, both approaches compare favourably to other well-known frameworks. They also identify a number of genes that are strong candidates for further study for involvement in this disease.

DNA Assembly is among the most fundamental and challenging problems in bioinformatics. Near optimal solutions are available for bacterial and small genomes. However assembling large and complex genomes including the human genome using Next-Generation-Sequencing (NGS) technologies is shown to be very difficult. This paper presents an algorithm for creating contigs from NGS short read data that is capable of working with multiple k-mer lengths and introduces a technique to combine contigs generated from different k runs with results from other assemblers in order to obtain significantly better assemblies. Experimental results from 9 real datasets show an increase in N50 value by a factor of 3, when combining newly created contigs with results from other assemblers.

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

The Fragment Assembly Problem is a major component of the DNA sequencing process that is identified as being NP-Hard. A variety of approaches to this problem have been used, including overlap-layout-consensus, de Bruijn graphs, and greedy graph based algorithms. The overlap-layout-consensus approach is one of the more popular strategies which has been studied on a collection of heuristics and metaheuristics. In this study heuristics and Genetic Algorithm variations are combined to exploit their respective benefits. These algorithms were able to produce results that surpassed the best results obtained by a collection of state-of-the-art metaheuristics on ten of sixteen popular benchmark data sets.

Experimental extended x-ray absorption fine structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, predicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo Optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proved somewhat successful in structure refinement but have not been successful in finding the global minima. Based on the success of using evolutionary algorithms to overcome local minima issues in other domains, we propose multiple approaches to better predict the structure of metal protein complexes; genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE).

Quaternary error-correcting codes defined over the edit metric may be used as labels to track the origin of sequence data. When used in such applications there are typically additional restrictions that are biologically motivated, such as a required GC content or the avoidance of certain patterns. As a result such codes can not be expected to have a regular structure, making decoding particularly challenging. Previous work on decoding edit codes considered the use of side effect machines for decoding, successfully decoding up to 93.86% of error vectors. In this study the recentering/restarting algorithm is used in combination with side effect machines and an alternative representation based upon transpositions. Using the same data as in the previous work, the rate of successful decoding was significantly improved, with many cases obtaining rates very close to 100%.

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find and report preliminary results on a synthetic dataset and some biological benchmarking suites. We obtain results that are comparable to the best motif discovery algorithms available. We conclude that since our approach finds multiple degenerate motifs in one run it could benefit from using some post processing technique to cluster the output, allowing it to be tested on larger datasets and to obtain more accurate performance feedback.

Error-correcting codes allow for reliable transmission of data over mediums subject to interference. They guarantee detection and recovery from a level of transmission corruption. Larger error-correcting codes increase the maximum sizes of messages transmittable, which improves communication efficiency. However, discovering optimal error-correcting codes for different code specifications is equivalent to the NP-Hard problem of determining maximum cliques of a graph.

We present a novel method for the creation of photographic mosaic images using fractals generated via evolutionary techniques. A photomosaic is a rendering of an image performed by placing a grid of smaller images that permit the original image to be visible when viewed from a distance. The problem of selecting the smaller images is a computationally intensive one. In this study we use an evolutionary algorithm to create fractal images on demand to generate tiles of the photomosaic. A number of images and tile resolutions are tested yielding acceptable results.

DNA error correcting codes over the edit metric can be used to correct sequencing errors. The codewords may be used as embeddable markers that allow one to track the origin of sequence data. The Salmon Algorithm is a search meta-heuristic inspired by the behaviour of salmon swimming upstream to spawn. This algorithm consists of a number of parameters, which we tune for the purpose of constructing DNA error correcting codes with a large number of codewords. Using this algorithm, several best known code sizes are improved. Construction of codes obeying biological restrictions is also discussed and the use of the Salmon Algorithm for this purpose is demonstrated.

DNA edit metric codes are used as labels to track the origin of sequence data. This study is the first to treat sophisticated decoders for these error-correcting codes. Side effect machines can provide efficient decoding algorithms for such codes. Two methods for automatically producing decoding algorithms are presented. Side Effect Machines (SEMs), generalizations of finite state automata, are used in both. Single Classifier Machines (SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines (LSEMs) use multiple side effect machines to create a tree structured iterated classification. This study examines these techniques and provides new decoders for existing codes. Presented are ideas for best practises for the creation of these two types of new edit metric decoders. Codes of the form (n,M,d)4are used in testing due to their suitability for bioinformatics problems. A group of (12, 54-56, 7)4codes are used as an example of the process.

DNA error correcting codes over the edit metric create embeddable markers for sequencing projects that are tolerant of sequencing errors. When a sequence library has multiple sources for its sequences, use of embedded markers permit tracking of sequence origin. Evolutionary algorithms are currently the best known technique for optimizing DNA error correcting codes. In this study we resolve the question of the utility of the crossover operator used in earlier studies on optimizing DNA error correcting codes. The crossover operator in question is found to be substantially counterproductive. A majority of crossover events produce results that violate minimum-distance constraints required for error correction. A new algorithm, a form of modified evolution strategy, is tested and is found to locate codes with record size. The table of best know sizes for DNA-error correcting codes is updated.

We provide a preliminary exploration of the use of genetic algorithms (GA) upon a substitution permutation network (SPN) cipher. The purpose of the exploration is to determine how to find weak keys. The size of the selected SPN created by Stinson gives a sample for showing the methodology and suitability of an attack using GA. We divide the types of keys into groups, each of which is analyzed to determine which groups are weaker. Simple genetic operators are examined to show the suitability of GA when applied to this problem. Results show the potential of GA to provide automated or computer assisted breaking of ciphers. The GA broke a subset of the keys using small input texts.

An extremal self-dual doubly-even binary (n,k,d) code has a minimum weight d=4/spl lfloor/n/24/spl rfloor/+4. Of such codes with length divisible by 24, the Golay code is the only (24,12,8) code, the extended quadratic residue code is the only known (48,24,12) code, and there is no known (72,36,16) code. One may partition the search for a (48,24,12) self-dual doubly-even code into three cases. A previous search assuming one of the cases found only the extended quadratic residue code. We examine the remaining two cases. Separate searches assuming each of the remaining cases found no codes and thus the extended quadratic residue code is the only doubly-even self-dual (48,24,12) code.