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.

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.

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.

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.

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.

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.