A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion.

The use of machine learning (ML) in anaerobic digestion (AD) is growing in popularity and improves the interpretation of complex system parameters for better operation and optimisation. This systematic literature review aims to explore how ML is currently employed in AD, with particular attention to the challenges of implementation and the benefits of integrating ML techniques. While both lab and industry-scale datasets have been used for model training, challenges arise from varied system designs and the different monitoring equipment used. Traditional machine-learning techniques, predominantly artificial neural networks (ANN), are the most commonly used but face difficulties in scalability and interpretability. Specifically, models trained on lab-scale data often struggle to generalize to full-scale, real-world operations due to the complexity and variability in bacterial communities and system operations. In practical scenarios, machine learning can be employed in real-time operations for predictive modelling, ensuring system stability is maintained, resulting in improved efficiency of both biogas production and waste treatment processes. Through reviewing the ML techniques employed in wider applied domains, potential future research opportunities in addressing these challenges have been identified.

A Generalised Dropout Mechanism for Distributed Systems.

This letter uses a modified form of the NK model introduced to explore aspects of distributed control. In particular, a previous result suggesting the use of dynamically formed subgroups within the overall system can be more effective than global control is further explored. The conditions under which the beneficial distributed control emerges are more clearly identified, and the reason for the benefit over traditional global control is suggested as a generally applicable dropout mechanism to improve learning in such systems.

Are Artificial Dendrites Useful in Neuro-Evolution?

The significant role of dendritic processing within neuronal networks has become increasingly clear. This letter explores the effects of including a simple dendrite-inspired mechanism into neuro-evolution. The phenomenon of separate dendrite activation thresholds on connections is allowed to emerge under an evolutionary process. It is shown how such processing can be positively selected for, particularly for connections between the hidden and output layers, and increases performance.

On the Emergence of Intersexual Selection: Arbitrary Trait Preference Improves Female-Male Coevolution.

Sexual selection is a fundamental aspect of evolution for all eukaryotic organisms with mating types. This article suggests intersexual selection is best viewed as a mechanism with which to compensate for the unavoidable dynamics of coevolution between sexes that emerge with isogamy. Using the NKCS model it is shown by varying fitness landscape size, ruggedness, and connectedness, how a purely arbitrary trait preference sexual selection mechanism proves beneficial with high dependence between the sexes. This is found to be the case whether one or both sexes exploit such intersexual selection.

On coevolution: Asymmetry in the NKCS model.

The NKCS model was introduced to explore coevolutionary systems, that is, systems in which multiple species are closely interconnected. The fitness landscapes of the species are coupled to a controllable amount, where the underlying properties of the individual landscapes are also controllable. Previous work has assumed symmetry with respect to the controlling parameters. This paper explores the effects of reducing that symmetry on the behaviour of the coevolutionary system, including varying genome complexity, the degree of landscape coupling, and the use of local learning. Significant changes in behaviour from the traditional model are seen across the parameter space. These findings are suggested as particularly pertinent to symbiotic relationships.

In silico optimization of cancer therapies with multiple types of nanoparticles applied at different times.

BACKGROUND AND OBJECTIVE: Cancer tumors constitute a complicated environment for conventional anti-cancer treatments to confront, so solutions with higher complexity and, thus, robustness to diverse conditions are required. Alternations in the tumor composition have been documented, as a result of a conventional treatment, making an ensemble of cells drug resistant. Consequently, a possible answer to this problem could be the delivery of the pharmaceutic compound with the assistance of nano-particles (NPs) that modify the delivery characteristics and biodistribution of the therapy. Nonetheless, to tackle the dynamic response of the tumor, a variety of application times of different types of NPs could be a way forward. METHODS: The in silico optimization was investigated here, in terms of the design parameters of multiple NPs and their application times. The optimization methodology used an open-source simulator to provide the fitness of each possible treatment. Because the number of different NPs that will achieve the best performance is not known a priori, the evolutionary algorithm utilizes a variable length genome approach, namely a metameric representation and accordingly modified operators. RESULTS: The results highlight the fact that different application times have a significant effect on the robustness of a treatment. Whereas, applying all NPs at earlier time slots and without the ordered sequence unveiled by the optimization process, proved to be less effective. CONCLUSIONS: The design and development of a dynamic tool that will navigate through the large search space of possible combinations can provide efficient solutions that prove to be beyond human intuition.

Towards an evolvable cancer treatment simulator.

The use of high-fidelity computational simulations promises to enable high-throughput hypothesis testing and optimisation of cancer therapies. However, increasing realism comes at the cost of increasing computational requirements. This article explores the use of surrogate-assisted evolutionary algorithms to optimise the targeted delivery of a therapeutic compound to cancerous tumour cells with the multicellular simulator, PhysiCell. The use of both Gaussian process models and multi-layer perceptron neural network surrogate models are investigated. We find that evolutionary algorithms are able to effectively explore the parameter space of biophysical properties within the agent-based simulations, minimising the resulting number of cancerous cells after a period of simulated treatment. Both model-assisted algorithms are found to outperform a standard evolutionary algorithm, demonstrating their ability to perform a more effective search within the very small evaluation budget. This represents the first use of efficient evolutionary algorithms within a high-throughput multicellular computing approach to find therapeutic design optima that maximise tumour regression.

The Evolution of Sex through the Baldwin Effect.

This article suggests that the fundamental haploid-diploid cycle of eukaryotic sex exploits a rudimentary form of the Baldwin effect. With this explanation for the basic cycle, the other associated phenomena can be explained as evolution tuning the amount and frequency of learning experienced by an organism. Using the well-known NK model of fitness landscapes, it is shown that varying landscape ruggedness varies the benefit of the haploid-diploid cycle, whether based upon endomitosis or syngamy. The utility of pre-meiotic doubling and recombination during the cycle are also shown to vary with landscape ruggedness. This view is suggested as underpinning, rather than contradicting, many existing explanations for sex.

On Design Mining: Coevolution and Surrogate Models.

Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation. In this article, we focus upon the coevolutionary nature of the design process when it is decomposed into concurrent sub-design-threads due to the overall complexity of the task. Using an abstract, tunable model of coevolution, we consider strategies to sample subthread designs for whole-system testing and how best to construct and use surrogate models within the coevolutionary scenario. Drawing on our findings, we then describe the effective design of an array of six heterogeneous vertical-axis wind turbines.

Towards a Physarum learning chip.

Networks of protoplasmic tubes of organism Physarum polycehpalum are macro-scale structures which optimally span multiple food sources to avoid repellents yet maximize coverage of attractants. When data are presented by configurations of attractants and behaviour of the slime mould is tuned by a range of repellents, the organism preforms computation. It maps given data configuration into a protoplasmic network. To discover physical means of programming the slime mould computers we explore conductivity of the protoplasmic tubes; proposing that the network connectivity of protoplasmic tubes shows pathway-dependent plasticity. To demonstrate this we encourage the slime mould to span a grid of electrodes and apply AC stimuli to the network. Learning and weighted connections within a grid of electrodes is produced using negative and positive voltage stimulation of the network at desired nodes; low frequency (10 Hz) sinusoidal (0.5 V peak-to-peak) voltage increases connectivity between stimulated electrodes while decreasing connectivity elsewhere, high frequency (1000 Hz) sinusoidal (2.5 V peak-to-peak) voltage stimulation decreases network connectivity between stimulated electrodes. We corroborate in a particle model. This phenomenon may be used for computation in the same way that neural networks process information and has the potential to shed light on the dynamics of learning and information processing in non-neural metazoan somatic cell networks.

On Cellular Darwinism: Mitochondria.

The significant role of mitochondria within cells is becoming increasingly clear. This letter uses the NKCS model of coupled fitness landscapes to explore aspects of organelle-nucleus coevolution. The phenomenon of mitochondrial diversity is allowed to emerge under a simple intracellular evolutionary process, including varying the relative rate of evolution by the organelle. It is shown how the conditions for the maintenance of more than one genetic variant of mitochondria are similar to those previously suggested as needed for the original symbiotic origins of the relationship using the NKCS model.

Design Mining Interacting Wind Turbines.

An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated wind conditions after being physically instantiated by a 3D printer. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.

On the Evolution of Behaviors through Embodied Imitation.

This article describes research in which embodied imitation and behavioral adaptation are investigated in collective robotics. We model social learning in artificial agents with real robots. The robots are able to observe and learn each others' movement patterns using their on-board sensors only, so that imitation is embodied. We show that the variations that arise from embodiment allow certain behaviors that are better adapted to the process of imitation to emerge and evolve during multiple cycles of imitation. As these behaviors are more robust to uncertainties in the real robots' sensors and actuators, they can be learned by other members of the collective with higher fidelity. Three different types of learned-behavior memory have been experimentally tested to investigate the effect of memory capacity on the evolution of movement patterns, and results show that as the movement patterns evolve through multiple cycles of imitation, selection, and variation, the robots are able to, in a sense, agree on the structure of the behaviors that are imitated.

Evolving functional and structural dynamism in coupled Boolean networks.

This article uses a recently presented abstract, tunable Boolean regulatory network model to further explore aspects of mobile DNA, such as transposons. The significant role of mobile DNA in the evolution of natural systems is becoming increasingly clear. This article shows how dynamically controlling network node connectivity and function via transposon-inspired mechanisms can be selected for to significant degrees under coupled regulatory network scenarios, including when such changes are heritable. Simple multicellular and coevolutionary versions of the model are considered.

Evolving spiking networks with variable resistive memories.

Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types.

Evolving Boolean regulatory networks with epigenetic control.

The significant role of epigenetic mechanisms within natural systems has become increasingly clear. This paper uses a recently presented abstract, tunable Boolean genetic regulatory network model to explore aspects of epigenetics. It is shown how dynamically controlling transcription via a DNA methylation-inspired mechanism can be selected for by simulated evolution under various single and multicellular scenarios. Further, it is shown that the effects of such control can be inherited without detriment to fitness.

Dynamical genetic programming in XCSF.

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifier system. In particular, dynamical arithmetic networks are used to represent the traditional condition-action production system rules to solve continuous-valued reinforcement learning problems and to perform symbolic regression, finding competitive performance with traditional genetic programming on a number of composite polynomial tasks. In addition, the network outputs are later repeatedly sampled at varying temporal intervals to perform multistep-ahead predictions of a financial time series.

Evolving Boolean networks with structural dynamism.

This short article presents an abstract, tunable model of genomic structural change within the cell life cycle and explores its use with simulated evolution. A well-known Boolean model of genetic regulatory networks is extended to include changes in node connectivity based upon the current cell state to begin to capture some of the effects of transposable elements. The evolvability of such networks is explored using a version of the NK model of fitness landscapes with both synchronous and asynchronous updating. Structural dynamism is found to be selected for in nonstationary environments with both update schemes and subsequently shown capable of providing a mechanism for evolutionary innovation when such reorganizations are inherited. This is also found to be the case in stationary environments with asynchronous updating.

A simple computational cell: coupling boolean gene and protein networks.

This article presents an abstract, tunable model containing two of the principal information-processing features of cells and explores its use with simulated evolution. The random Boolean model of genetic regulatory networks is extended to include a protein interaction network. The underlying behavior of the resulting two coupled dynamical networks is investigated before their evolvability is explored using a version of the NK model of fitness landscapes.