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In recent years, research on Physarum polycephalum has become more popular after Nakagaki et al. (2000) performed their famous experiment showing that Physarum was able to find the shortest route through a maze. Subsequent researches have confirmed the ability of Physarum-inspired algorithms to solve a wide range of NP-hard problems. In contrast to previous reviews that either focus on biological aspects or bio-inspired applications, here we present a comprehensive review that highlights recent Physarum polycephalum biological aspects, mathematical models, and Physarum bio-inspired algorithms and their applications. The novelty of this review stems from our exploration of Physarum intelligent behaviour in competition settings. Further, we have presented our new model to simulate Physarum in competition, where multiple Physarum interact with each other and with their environments. The bio-inspired Physarum in competition algorithms proved to have great potentials for future research., arXiv admin note: text overlap with arXiv:1712.02910 by other authors

Artificial Intelligence (AI) is a high-profile subject these days. In its brief history it has undergone several highs and lows and suffered from significant degrees of hype as well as antagonism and fear. One thing is clear: we are no closer to the goal of producing a truly sentient being than when it started. Nonetheless, the tools developed by AI researchers are here to stay and as with all technological advances it has its good and bad aspects. In this article I will present a brief overview of the field of AI looking at what it is, how it developed, what are its specialism as well as some of its well-publicised successes, and failures, as well as pointing out some key Christian participants in the story.

Support Vector Regression (SVR) and its variants are widely used regression algorithms, and they have demonstrated high generalization ability. This research proposes a new SVR-based regressor: v-minimum absolute deviation distribution regression (v-MADR) machine. Instead of merely minimizing structural risk, as with v-SVR, v-MADR aims to achieve better generalization performance by minimizing both the absolute regression deviation mean and the absolute regression deviation variance, which takes into account the positive and negative values of the regression deviation of sample points. For optimization, we propose a dual coordinate descent (DCD) algorithm for small sample problems, and we also propose an averaged stochastic gradient descent (ASGD) algorithm for large-scale problems. Furthermore, we study the statistical property of v-MADR that leads to a bound on the expectation of error. The experimental results on both artificial and real datasets indicate that our v-MADR has significant improvement in generalization performance with less training time compared to the widely used v-SVR, LS-SVR, ε-TSVR, and linear ε-SVR. Finally, we open source the code of v-MADR at https://github.com/AsunaYY/v-MADR for wider dissemination.

Recent years have seen a renewed interest in evolutionary computation applied to the automatic design of deep neural network architectures, i.e. Neural Architecture Search (NAS). The advantages of evolutionary approaches in NAS include their conceptual simplicity and their flexibility with regards to search space definition and/or optimization objective.However, Artificial Immune Systems (AIS) that follow the evolutionary computation paradigm are less explored in NAS. In this research, we aim to leverage their intrinsic and excellent ability to balance performance and population diversity to develop a novel Neural Ensemble Search method, based on the Clonal Selection Algorithm [1]. For more generality, we focus on designing macro-architectures rather than architectural components.Experiments on popular computer vision benchmarks demonstrate that our method reaches competitive accuracy and efficiency despite minimal augmentation and post-processing. We show that the AIS brings tangible benefits, including maintaining the diversity of solutions, a semantically straightforward implementation, and high efficiency. Moreover, this AIS can exhibit a "secondary response": when presented with a related but more difficult task, the ensemble will perform competently with zero modification to the architectures or the training protocol.

Data clustering is a well-researched area in data mining and machine learning. The clustering algorithms that can handle both numeric and categorical variables have been extensively researched in the recent years. However, the clustering algorithms have a major limitation that converge to a local optima. Therefore, to address this problem this paper has proposed a novel algorithm ABC k-prototypes (Artificial Bee Colony clustering based on k-prototypes) for clustering mixed data. In our proposed approach we use the combination between the distribution centroid and the mean to calculate the dissimilarity between data objects and prototypes. The proposed algorithm is tested on five different datasets taken from the UCI machine learning data repository. The comparative results in the performance measures of the clustering showed that the proposed algorithm outperformed the traditional k-prototypes.

Many real-world problems can be naturally formulated as discrete multi-objective optimization (DMOO) problems. In this research we propose a novel bio-inspired Physarum competition algorithm (PCA) to tackle DMOO problems by modelling the Physarum discrete motility over a hexagonal cellular automaton. Our algorithm is based on the chemo-attraction forces towards food resources (Objective Functions) and the repulsion negative forces between the competing Physarum. Numerical experimental work clearly demonstrated that our PCA algorithm had the best performance for the spread indicator against three state-of-the-art evolutionary algorithms, and its effectiveness in terms of commonly used metrics. These results have indicated the superiority of PCA in exploring the search space and keeping diversity, this makes PCA a promising algorithm for solving DMOO problems.

Abubakr Awad research is supported by Elphinstone PhD Scholarship (University of Aberdeen). Wei Pang, George Coghill, and David Lusseau are supported by the Royal Society International Exchange program (Grant Ref IE160806).

Wildlife tourism (WT) is an important economic sector globally, which can sustain national and local economies. These activities have been reconceptualised as consumptive because of their impacts on the wildlife, and the problem of managing WT as a common-pool resource issue. We use an individual-based model to simulate the dynamics of a WT destination in different development phases. We then ask if any of the governance structures commonly proposed to solve common pool resource issues are appropriate to sustainably manage a WT destination during its development. The level of specialisation of tourists visiting a destination can influence both the exploitation of the wildlife and the socio-economic success of the industry, and no single governance structure leads to sustainability in every stage of a WT destination lifecycle. Given the dynamics of WT destinations, an adaptive governance framework is crucial to avoid wildlife depletion and economic failure of the industry.

We propose a novel support vector regression approach called \(\varepsilon \)-Distance Weighted Support Vector Regression (\(\varepsilon \)-DWSVR). \(\varepsilon \)-DWSVR specifically addresses a challenging issue in support vector regression: how to deal with the situation when the distribution of the internal data in the \(\varepsilon \)-tube is different from that of the boundary data containing support vectors. The proposed \(\varepsilon \)-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle this issue. To solve the new optimization problem arising from \(\varepsilon \)-DWSVR, we adopt dual coordinate descent (DCD) with kernel functions for medium-scale problems and also employ averaged stochastic gradient descent (ASGD) to make \(\varepsilon \)-DWSVR scalable to larger problems. We report promising results obtained by \(\varepsilon \)-DWSVR in comparison with five popular regression methods on sixteen UCI benchmark datasets.

I would like to acknowledge the support given to me by the Engineering and Physical Sciences Research Council (EPSRC) DTP grant number EP/N509814/1.

Mobile wireless sensor networks (Mobile-WSN) are useful in harsh environments due to the presence of obstacles and/or dangerous for sensors to be deployed deterministically. In this paper, we proposed a Physarum inspired autonomous, model for dynamic deployment of sensor nodes where multiple Physarum (as representation of sensors) will compete over food resources (interest points) based on chemo-attraction, and repulsion forces exerted by competing Physarums and obstacles. Our simulation results have demonstrated the high coverage performance of the model with minimal move overhead in the presence of obstacles with the least number of sensors.

1. Nature-based recreation provides substantial benefits for human wellbeing, including improving physical and mental health, but can also have severe ecological impacts. The recreational value of landscapes and natural areas is often used to generate support for public spending in conservation. However, we still don’t know whether nature-based recreationists place greater recreational value on natural areas that have high conservation value compared to other green spaces. 2. Here, we determine which attributes of nature-based tourism destinations provide recreational services. We used pictures of wildlife posted on Flickr to quantify wildlife-watching activities in Scotland. We then determined the environmental variables key to attracting wildlife watchers to a destination, such as protected areas (PAs), the perceived naturalness, and the presence of different types of infrastructure. 3. Infrastructure is the best predictor of intensity of wildlife watching activities in Scotland, while areas of high natural value are rarely used. PAs act as a weak attractor of wildlife watchers, with PAs designated to protect threatened habitats or species having low recreational value. In accessible and highly visited areas, higher biodiversity increases the intensity of wildlife watching activities. 4. Synthesis and applications. Areas of high natural and conservation value and areas of high recreational value do not tend to overlap. Recreational ecosystem services are mainly provided by the wider countryside and highly transformed landscapes as opposed to wild ecosystems and PAs designated to protect environmental features of high conservation value. These results question the synergy between the goals of recreation and those of conservation and the use of recreation as a justification for economic investment in conservation. During wildlife watching activities most people experience an urbanised, highly transformed nature; it will be important to determine how this human-dominated nature can influence support for conservation of wild and remote areas.

In this paper, a novel qualitative differential equation model learning (QML) framework named QML-Morven is presented. QML-Morven employs both symbolic and evolutionary approaches as its learning strategies to deal with models of different complexity. Based on this framework, a series of experiments were designed and carried out to: (1) investigate factors that influence the learning precision and minimum data requirement for successful learning; (2) address the scalability issue of QML systems.

The Artificial Reaction Network (ARN) is an artificial chemistry inspired by Cell Signalling Networks. Its purpose is to represent chemical circuitry and to explore the computational properties responsible for generating emergent high-level behaviour. In this paper, the design and application of ARN-based cell-like agents termed "Cytobots" are explored. Such agents provide a facility to explore the dynamics and emergent properties of multicellular systems. The Cytobot ARN is constructed by combining functional motifs found in real biochemical networks. By instantiating this ARN, multiple Cytobots are created, each of which is capable of recognising environmental patterns, stigmergic communication with others and controlling its own trajectory. Applications in biological simulation and robotics are investigated by first applying the agents to model the life-cycle phases of the cellular slime mould D. discoideum and then to simulate an oil-spill clean-up operation. The results demonstrate that an ARN-based approach provides a powerful tool for modelling multi-agent biological systems and also has application in swarm robotics.

The Artificial Reaction Network (ARN) is a Cell Signalling Network inspired connectionist representation belonging to the branch of A-Life known as Artificial Chemistry. Its purpose is to represent chemical circuitry and to explore computational properties responsible for generating emergent high-level behaviour associated with cells. In this paper, the computational mechanisms involved in pattern recognition and spatio-temporal pattern generation are examined in robotic control tasks. The results show that the ARN has application in limbed robotic control and computational functionality in common with Artificial Neural Networks. Like spiking neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network, however it offers increased flexibility. Furthermore, the results illustrate parallels between emergent neural and cell intelligence.

Big data offer a great opportunity for nature-based recreation (NbR) mapping and evaluation. However, it is important to determine when and how it is appropriate to use this resource. We used Scotland as a case study to validate the use of data from Flickr as an indicator of NbR on a national scale and at several regional spatial and temporal resolutions. We compared Flickr photographs to visitor statistics in the Cairngorms National Park (CNP) and determined whether temporal variability in photo counts could be explained by known annual estimates of CNP visitor numbers. We then used a unique recent national survey of nature recreation in Scotland to determine whether the spatial distribution of Flickr photos could be explained by known spatial variability in nature use. Following this validation work, we used Flickr data to identify hotspots of wildlife watching in Scotland and investigated how they changed between 2005 and 2015.We found that spatial and temporal patterns in Flickr count are explained by measures of visitation obtained through surveys and that this relationship is reliable down to a 10 Km scale resolution. Our findings have implications for planning and management of NbR as they suggest that photographs uploaded on Flickr reflect patterns of NbR at spatial and temporal scales that are relevant for ecosystem management.

The Dendritic Cell Algorithm (DCA) and its improved version: Deterministic Dendritic Cell Algorithm (dDCA) are essentially binary classification algorithms based on the behavior of Dendritic Cells (DCs) in the immune system. Both DCA and dDCA collect and process the data in form of signals, and produce output signal. The signals are divided in two types: danger and safe signals, and the output signal is determined by the values of the danger and safe signals. However, both DCA and dDCA suffer from data misclassification due to their sensitivity to data order. In this research we proposed a Fuzzy Deterministic Dendritic Cell Algorithm (FdDCA), which combines dDCA, fuzzy sets, and K-means clustering. The main objective of this research is to smooth the sharp boundaries between signals since we cannot always identify a clear boundary between the values of the signals. Our approach fuzzifies the signal values using linguistic variables, and a rule base is built to support fuzzy inference. The experimental results based on real data sets show that our approach shows a promising results compared to DCA and dDCA