Project description:Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making.
Project description:As an important incomplete algorithm for solving Distributed Constraint Optimization Problems (DCOPs), local search algorithms exhibit the advantages of flexibility, high efficiency and high fault tolerance. However, the significant historical values of agents that affect the local cost and global cost are never taken into in existing incomplete algorithms. In this article, a novel Local Cost Simulation-based Algorithm named LCS is presented to exploit the potential of historical values of agents to further enhance the exploration ability of the local search algorithm. In LCS, the Exponential Weighted Moving Average (EWMA) is introduced to simulate the local cost to generate the selection probability of each value. Moreover, populations are constructed for each agent to increase the times of being selected inferior solutions by population optimization and information exchange between populations. We theoretically analyze the feasibility of EWMA and the availability of solution quality improvement. In addition, based on our extensive empirical evaluations, we experimentally demonstrate that LCS outperforms state-of-the-art DCOP incomplete algorithms.
Project description:In this paper, we introduce general idea of trajectories attraction in phase space, which is very common phenomenon for the processes in the Nature. We start from a rather general biological example of natural selection, where adaptation to the environmental conditions can be described as attraction of some population distribution in the phenotype space to a center of ecological niche. The niche is mathematically represented as the "survival coefficient" which in turn can be linked to a kind of energy potential. This link between biological and physical approaches may be very useful for solution of a wide range of biological problems. In particular, we discuss an evolution in complex potential with a lot of valleys in a multi-dimensional space accompanied by the so-called large river effect, which corresponds to an extremely slow evolution of some, normally close to final, stages of the adaptation. This effect is related to the practically important states of the "frozen kinetics" which accompanies extremely wide spectrum of phenomena and allows understanding different physical and biological processes.
Project description:Chemical and molecular-based computers may be promising alternatives to modern silicon-based computers. In particular, hybrid systems, where tasks are split between a chemical medium and traditional silicon components, may provide access and demonstration of chemical advantages such as scalability, low power dissipation, and genuine randomness. This work describes the development of a hybrid classical-molecular computer (HCMC) featuring an electrochemical reaction on top of an array of discrete electrodes with a fluorescent readout. The chemical medium, optical readout, and electrode interface combined with a classical computer generate a feedback loop to solve several canonical optimization problems in computer science such as number partitioning and prime factorization. Importantly, the HCMC makes constructive use of experimental noise in the optical readout, a milestone for molecular systems, to solve these optimization problems, as opposed to in silico random number generation. Specifically, we show calculations stranded in local minima can consistently converge on a global minimum in the presence of experimental noise. Scalability of the hybrid computer is demonstrated by expanding the number of variables from 4 to 7, increasing the number of possible solutions by 1 order of magnitude. This work provides a stepping stone to fully molecular approaches to solving complex computational problems using chemistry.
Project description:We presented small groups of chimpanzees with two collective action situations, in which action was necessary for reward but there was a disincentive for individuals to act owing to the possibility of free-riding on the efforts of others. We found that in simpler scenarios (experiment 1) in which group size was small, there was a positive relationship between rank and action with more dominant individuals volunteering to act more often, particularly when the reward was less dispersed. Social tolerance also seemed to mediate action whereby higher tolerance levels within a group resulted in individuals of lower ranks sometimes acting and appropriating more of the reward. In more complex scenarios, when group size was larger and cooperation was necessary (experiment 2), overcoming the problem was more challenging. There was highly significant variability in the action rates of different individuals as well as between dyads, suggesting success was more greatly influenced by the individual personalities and personal relationships present in the group.
Project description:Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus herd immunity optimizer (CHIO) algorithm is used to boost the efficiency of the probabilistic neural network (PNN) when solving classification problems. First, the PNN produces a random initial solution and submits it to the CHIO, which then attempts to refine the PNN weights. This is accomplished by the management of random phases and the effective identification of a search space that can probably decide the optimal value. The proposed CHIO-PNN approach was applied to 11 benchmark datasets to assess its classification accuracy, and its results were compared with those of the PNN and three methods in the literature, the firefly algorithm, African buffalo algorithm, and β-hill climbing. The results showed that the CHIO-PNN achieved an overall classification rate of 90.3% on all datasets, at a faster convergence speed as compared outperforming all the methods in the literature.Supplementary informationThe online version contains supplementary material available at 10.1007/s00500-022-06917-z.
Project description:Validating scales for clinical use is a common procedure in medicine and psychology. Through the application of computational methods, we present a new strategy for estimating construct validity and criterion validity. XGBoost, Random Forest and Support-Vector machine learning algorithms were employed in order to make predictions based on the pattern of participants' responses by systematically controlling computational experiments with artificial experiments whose results are guaranteed. According to these findings, these approaches are capable of achieving construct and criterion validity and therefore could provide an additional layer of evidence to traditional validation approaches. In particular, this study examined the extent to which measured items are inferable by theoretically related items, as well as the extent to which the information carried by a given construct can be translated into other theoretically compatible normative scales based on other constructs (thereby providing information about construct validity); as well as the replicability of clinical decision rules on several partitions (thereby providing information about criterion validity).
Project description:Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test problems and comparisons with other recent methods illustrate the effectiveness of the proposed integrated mechanism of different search strategies and demonstrate the performance superiority of the proposed algorithm.
Project description:Visual simulation - i.e., using internal reconstructions of the world to experience potential future versions of events that are not currently happening - is among the most sophisticated capacities of the human mind. But is this ability in fact uniquely human? To answer this question, we tested monkeys on a series of experiments involving the 'Planko' game, which we have previously used to evoke visual simulation in human participants. We found that monkeys were able to successfully play the game using a simulation strategy, predicting the trajectory of a ball through a field of planks while demonstrating a level of accuracy and behavioral signatures comparable to humans. Computational analyses further revealed that the monkeys' strategy while playing Planko aligned with a recurrent neural network (RNN) that approached the task using a spontaneously learned simulation strategy. Finally, we carried out awake functional magnetic resonance imaging while monkeys played Planko. We found activity in motion-sensitive regions of the monkey brain during hypothesized simulation periods, even without any perceived visual motion cues. This neural result closely mirrors previous findings from human research, suggesting a shared mechanism of visual simulation across species. In all, these findings challenge traditional views of animal cognition, proposing that nonhuman primates possess a complex cognitive landscape, capable of invoking imaginative and predictive mental experiences to solve complex everyday problems.
Project description:Electron paramagnetic resonance (EPR) spectroscopy is a very powerful biophysical tool that can provide valuable structural and dynamic information about a wide variety of biological systems. The intent of this review is to provide a general overview for biochemists and biological researchers of the most commonly used EPR methods and how these techniques can be used to answer important biological questions. The topics discussed could easily fill one or more textbooks; thus, we present a brief background on several important biological EPR techniques and an overview of several interesting studies that have successfully used EPR to solve pertinent biological problems. The review consists of the following sections: an introduction to EPR techniques, spin-labeling methods, and studies of naturally occurring organic radicals and EPR active transition metal systems that are presented as a series of case studies in which EPR spectroscopy has been used to greatly further our understanding of several important biological systems.