An introduction to simulated evolutionary optimization

Hybrid whale algorithm with evolutionary strategies and filtering for high-dimensional optimization: Application to microarray cancer data

The standard whale algorithm is prone to suboptimal results and inefficiencies in high-dimensional search spaces. Therefore, examining the whale optimization algorithm components is critical. The computer-generated initial populations often exhibit an uneven distribution in the solution space, leading to low diversity. We propose a fusion of this algorithm with a discrete recombinant evolutionary strategy to enhance initialization diversity. We conduct simulation experiments and compare the proposed algorithm with the original WOA on thirteen benchmark test functions. Simulation experiments on unimodal or multimodal benchmarks verified the better performance of the proposed RESHWOA, such as accuracy, minimum mean, and low standard deviation rate. Furthermore, we performed two data reduction techniques, Bhattacharya distance and signal-to-noise ratio. Support Vector Machine (SVM) excels in dealing with high-dimensional datasets and numerical features. When users optimize the parameters, they can significantly improve the SVM's performance, even though it already works well with its default settings. We applied RESHWOA and WOA methods on six microarray cancer datasets to optimize the SVM parameters. The exhaustive examination and detailed results demonstrate that the new structure has addressed WOA's main shortcomings. We conclude that the proposed RESHWOA performed significantly better than the WOA.

Near-Infrared Spectroscopy and Machine Learning for Accurate Dating of Historical Books

Non-destructive, fast, and accurate methods of dating are highly desirable for many heritage objects. Here, we present and critically evaluate the use of near-infrared (NIR) spectroscopic data combined with three supervised machine learning methods to predict the publication year of paper books dated between 1851 and 2000. These methods provide different accuracies; however, we demonstrate that the underlying processes refer to common spectral features. Regardless of the machine learning method used, the most informative wavelength ranges can be associated with C-H and O-H stretching first overtone, typical of the cellulose structure, and N-H stretching first overtone from amide/protein structures. We find that the expected influence of degradation on the accuracy of prediction is not meaningful. The variance-bias decomposition of the reducible error reveals some differences among the three machine learning methods. Our results show that two out of the three methods allow predictions of publication dates in the period 1851-2000 from NIR spectroscopic data with an unprecedented accuracy of up to 2 years, better than any other non-destructive method applied to a real heritage collection.

Multiobjective Sine Cosine Algorithm for Remote Sensing Image Spatial-Spectral Clustering

Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing image clustering, in essence, belongs to a complex optimization problem, due to the high dimensionality and complexity of remote sensing imagery. Therefore, it can be easily affected by the initial values and trapped in locally optimal solutions. Meanwhile, remote sensing images contain complex and diverse spatial-spectral information, which makes them difficult to model with only a single objective function. Although evolutionary multiobjective optimization methods have been presented for the clustering task, the tradeoff between the global and local search abilities is not well adjusted in the evolutionary process. In this article, in order to address these problems, a multiobjective sine cosine algorithm for remote sensing image data spatial-spectral clustering (MOSCA_SSC) is proposed. In the proposed method, the clustering task is converted into a multiobjective optimization problem, and the Xie-Beni (XB) index and Jeffries-Matusita (Jm) distance combined with the spatial information term (SI_Jm measure) are utilized as the objective functions. In addition, for the first time, the sine cosine algorithm (SCA), which can effectively adjust the local and global search capabilities, is introduced into the framework of multiobjective clustering for continuous optimization. Furthermore, the destination solution in the SCA is automatically selected and updated from the current Pareto front through employing the knee-point-based selection approach. The benefits of the proposed method were demonstrated by clustering experiments with ten UCI datasets and four real remote sensing image datasets.

Ellipse of uncertainty based algorithm for quantitative evaluation of defect localization using Lamb waves

Measurement deviation of time of flight (ToF) is inevitable in nondestructive testing based on the sparse array and ultrasonic Lamb waves. It affects the influence zone of temporal-spatial mapping trajectories (TSMTs) of signal parameters in the imaging zone, and further limits the quantitative evaluation of defect localization. In the paper, the ellipse of uncertainty (EOU) of TSMTs was derived from multiple parameters, including the group velocity, ToFs and their measurement deviations, distances between actuators and receivers. Then, an EOU-based algorithm was developed for quantitative evaluation of defect localization. The defects were localized by searching the individual scatterers at the intersection of multiple TSMTs. Based on the eccentricity of the uncertainty ellipse, a fuzzy scaling factor was introduced. It was combined with a fuzzy control parameter to tune the influence zone of TSMTs. Based on the acoustic reciprocity theorem and the fuzzy control parameter, the ToFs of scattering waves were fused to establish the one-to-one relation between individual scatterers and inspection pairs. Experimental results showed that the EOU-based algorithm can reduce the interferences of EOU in the detection; and the quantitative evaluation of defect localization was realized by analyzing the distribution of individuals and their ToF difference to inspection pairs.

Computing With Networks of Chemical Oscillators and its Application for Schizophrenia Diagnosis

Chemical reactions are responsible for information processing in living organisms, yet biomimetic computers are still at the early stage of development. The bottom-up design strategy commonly used to construct semiconductor information processing devices is not efficient for chemical computers because the lifetime of chemical logic gates is usually limited to hours. It has been demonstrated that chemical media can efficiently perform a specific function like labyrinth search or image processing if the medium operates in parallel. However, the number of parallel algorithms for chemical computers is very limited. Here we discuss top-down design of such algorithms for a network of chemical oscillators that are coupled by the exchange of reaction activators. The output information is extracted from the number of excitations observed on a selected oscillator. In our model of a computing network, we assume that there is an external factor that can suppress oscillations. This factor can be applied to control the nodes and introduce input information for processing by a network. We consider the relationship between the number of oscillation nodes and the network accuracy. Our analysis is based on computer simulations for a network of oscillators described by the Oregonator model of a chemical oscillator. As the example problem that can be solved with an oscillator network, we consider schizophrenia diagnosis on the basis of EEG signals recorded using electrodes located at the patient’s scalp. We demonstrated that a network formed of interacting chemical oscillators can process recorded signals and help to diagnose a patient. The parameters of considered networks were optimized using an evolutionary algorithm to achieve the best results on a small training dataset of EEG signals recorded from 45 ill and 39 healthy patients. For the optimized networks, we obtained over 82% accuracy of schizophrenia detection on the training dataset. The diagnostic accuracy can be increased to almost 87% if the majority rule is applied to answers of three networks with different number of nodes.

Self-Adaptive Particle Swarm Optimization-Based Echo State Network for Time Series Prediction

Echo state networks (ESNs), belonging to the family of recurrent neural networks (RNNs), are suitable for addressing complex nonlinear tasks due to their rich dynamic characteristics and easy implementation. The reservoir of the ESN is composed of a large number of sparsely connected neurons with randomly generated weight matrices. How to set the structural parameters of the ESN becomes a difficult problem in practical applications. Traditionally, the design of the parameters of the ESN structure is performed manually. The manual adjustment of the ESN parameters is not convenient since it is an extremely challenging and time-consuming task. This paper proposes an ensemble of five particle swarm optimization (PSO) strategies to design the structure of ESN and then reduce the manual intervention in the design process. An adaptive selection mechanism is used for each particle in the evolution to select a strategy from the strategy candidate pool for evolution. In addition, leaky integration neurons are used as reservoir internal neurons, which are added within the adaptive mechanism for optimization. The root mean squared error (RMSE) is adopted as the evaluation criterion. The experimental results on Mackey-Glass time series benchmark dataset show that the proposed method outperforms other traditional evolutionary methods. Furthermore, experimental results on electrocardiogram dataset show that the proposed method on the ensemble of PSO displays an excellent performance on real-world problems.

Collaborative task scheduling with new task arrival in cloud manufacturing using improved multi-population biogeography-based optimization

Task scheduling is important in cloud manufacturing because of customers’ increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms.

A global neural network learning machine: Coupled integer and fractional calculus operator with an adaptive learning scheme

Find the global optimal solution of the model is one promising research topic in computational intelligent community. Dependent on analogies to natural processes, the evolutionary swarm intelligent algorithms are widely used for solving global optimization problems which directed by the fitness values. In this paper, we propose one efficient fractional global learning machine (Fragmachine) which includes two stages (descending and ascending) to determine the optimal search path. The neural network model is used to approach the given fitness value. Specifically, for the descending stage, the integer gradient of the network output with respect the current location is employed to find the next descending point, while for the ascending stage, the fractional gradient is implemented to climb and escape from the local optimal point. We further propose one adaptive learning rate during training which relies on both the current gradient (integer or fractional) information and the fitness value. Finally, a series of numerical experiments verify the effectiveness of the proposed algorithm, Fragmachine.

Evolutionary Strategy-Based Location Algorithm for High-Resolution Lamb Wave Defect Detection With Sparse Array

Intelligent defect location algorithms based on the times-of-flight (ToFs) of Lamb waves are attractive for nondestructive testing (NDT) and structural health monitoring (SHM) of structures with large geometric sizes. Unlike the classical imaging algorithm based on projecting the amplitude information of scattering signals into a discrete spatial grid on the structure via their propagation characteristics, intelligent defect location algorithms are more efficient in specific applications. In our previous work, an intelligent algorithm for the location of defects in plates was proposed by considering the statistical, diversity, and fuzzy characteristics of the classical defect location algorithm. This approach can realize the efficient location of different defects under a suitable parameter selection. However, interfering components remain in the results, which decreases the detection resolution. Because the measurement uncertainty is directly related to the time, an optimized intelligent location algorithm is provided for the efficient defect location with Lamb waves and a sparse transducer array in this study. The defect position is identified with high resolution by analyzing the distribution of individuals. Several specific data and a fuzzy control parameter are introduced to the proposed algorithm. The K-means algorithm was adopted to realize the adaptive updating of individuals. The influence of parameter values on the detection results was analyzed. A combined analysis of the individuals was provided to ensure the detection robustness by eliminating the influence of fuzzy control parameters on the detection. Compared with the elliptic imaging algorithm, the intelligent defect location algorithm has higher location resolution and executes approximately 65 times faster.

Artificial Neural Networks-Based Material Parameter Identification for Numerical Simulations of Additively Manufactured Parts by Material Extrusion

To be able to use finite element (FE) simulations in structural component development, experimental investigations for the characterization of the material properties are required to subsequently calibrate suitable material cards. In contrast to the commonly used computational and time-consuming method of parameter identification (PI) by using analytical and numerical optimizations with internal or commercial software, a more time-efficient method based on machine learning (ML) is presented. This method is applied to simulate the material behavior of additively manufactured specimens made of acrylonitrile butadiene styrene (ABS) under uniaxial stress in a structural simulation. By using feedforward artificial neural networks (FFANN) for the ML-based direct inverse PI process, various investigations were carried out on the influence of sampling strategies, data quantity and data preparation on the prediction accuracy of the NN. Furthermore, the results of hyperparameter (HP) search methods are presented and discussed and their influence on the prediction quality of the FFANN are critically evaluated. The investigations show that the NN-based method is applicable to the present use case and results in material parameters that lead to a lower error between experimental and calculated force-displacement curves than the commonly used optimization-based method.

A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

As a meta-heuristic algoriTthm, particle swarm optimization (PSO) has the advantages of having a simple principle, few required parameters, easy realization and strong adaptability. However, it is easy to fall into a local optimum in the early stage of iteration. Aiming at this shortcoming, this paper presents a hybrid multi-step probability selection particle swarm optimization with sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients (MPSPSO-ST), which can strengthen the overall performance of PSO to a large extent. Firstly, we propose a hybrid multi-step probability selection update mechanism (MPSPSO), which skillfully uses a multi-step process and roulette wheel selection to improve the performance. In order to achieve a good balance between global search capability and local search capability to further enhance the performance of the method, we also design sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients inspired by chaos mechanism and trigonometric functions, which are integrated into the MPSPSO-ST algorithm. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. To evaluate the effectiveness of the MPSPSO-ST algorithm, we conducted extensive experiments with 20 classic benchmark functions. The experimental results show that the MPSPSO-ST algorithm has faster convergence speed, higher optimization accuracy and better robustness, which is competitive in solving numerical optimization problems and outperforms a lot of classical PSO variants and well-known optimization algorithms.

Hybrid Classifier Ensemble for Imbalanced Data

The class imbalance problem has become a leading challenge. Although conventional imbalance learning methods are proposed to tackle this problem, they have some limitations: 1) undersampling methods suffer from losing important information and 2) cost-sensitive methods are sensitive to outliers and noise. To address these issues, we propose a hybrid optimal ensemble classifier framework that combines density-based undersampling and cost-effective methods through exploring state-of-the-art solutions using multi-objective optimization algorithm. Specifically, we first develop a density-based undersampling method to select informative samples from the original training data with probability-based data transformation, which enables to obtain multiple subsets following a balanced distribution across classes. Second, we exploit the cost-sensitive classification method to address the incompleteness of information problem via modifying weights of misclassified minority samples rather than the majority ones. Finally, we introduce a multi-objective optimization procedure and utilize connections between samples to self-modify the classification result using an ensemble classifier framework. Extensive comparative experiments conducted on real-world data sets demonstrate that our method outperforms the majority of imbalance and ensemble classification approaches.

An Improved Underwater Electric Field-Based Target Localization Combining Subspace Scanning Algorithm And Meta-EP PSO Algorithm

In this paper, we propose an improved three-dimensional underwater electric field-based target localization method. This method combines the subspace scanning algorithm and the meta evolutionary programming (meta-EP) particle swarm optimization (PSO) algorithm. The subspace scanning algorithm is applied as the evaluation function of the electric field-based underwater target locating problem. The meta-EP PSO method is used to select M elite particles by the q-tournament selection method, which could effectively reduce the computational complexity of the three-dimensional underwater target localization. Moreover, the proposed meta-EP PSO optimization algorithm can avoid subspace scanning trapping into local minima. We also analyze the positioning performance of the uniform circular and cross-shaped electrodes arrays by using the subspace scanning algorithm combined with meta–EP PSO. According to the simulation, the calculation amount of the proposed algorithm is greatly reduced. Moreover, the positioning accuracy is effectively improved without changing the positioning accuracy and search speed.

Applications of Information Theory Methods for Evolutionary Optimization of Chemical Computers

It is commonly believed that information processing in living organisms is based on chemical reactions. However, the human achievements in constructing chemical information processing devices demonstrate that it is difficult to design such devices using the bottom-up strategy. Here I discuss the alternative top-down design of a network of chemical oscillators that performs a selected computing task. As an example, I consider a simple network of interacting chemical oscillators that operates as a comparator of two real numbers. The information on which of the two numbers is larger is coded in the number of excitations observed on oscillators forming the network. The parameters of the network are optimized to perform this function with the maximum accuracy. I discuss how information theory methods can be applied to obtain the optimum computing structure.

Service-oriented Application Composition with Evolutionary Heuristics and Multiple Criteria

The need to create and deploy business application systems rapidly has sparked interest in using web services to compose them. When creating mission-critical business applications through web service compositions, in addition to ensuring that functional requirements are met, designers need to consider the end-to-end reliability, security, performance, and overall cost of the application. As the number of available coarse-grain business services grows, the problem of selecting appropriate services quickly becomes combinatorially explosive for realistic-sized business applications. This article develops a business-process-driven approach for composing service-oriented applications. We use a combination of weights to explore the entire QoS criteria landscape through the use of a multi-criteria genetic algorithm (GA) to identify a Pareto-optimal multidimensional frontier that permits managers to trade off conflicting objectives when selecting a set of services. We illustrate the effectiveness of the approach by applying it to a real-world drop-ship business application and compare its performance to another GA-based approach for service composition.

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.

SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents an overview of the constraint handling techniques. It also includes a hybrid algorithm, namely the Stochastic Ranking with Improved Firefly Algorithm (SRIFA) for solving constrained real-world engineering optimization problems. The stochastic ranking approach is broadly used to maintain balance between penalty and fitness functions. FA is extensively used due to its faster convergence than other metaheuristic algorithms. The basic FA is modified by incorporating opposite-based learning and random-scale factor to improve the diversity and performance. Furthermore, SRIFA uses feasibility based rules to maintain balance between penalty and objective functions. SRIFA is experimented to optimize 24 CEC 2006 standard functions and five well-known engineering constrained-optimization design problems from the literature to evaluate and analyze the effectiveness of SRIFA. It can be seen that the overall computational results of SRIFA are better than those of the basic FA. Statistical outcomes of the SRIFA are significantly superior compared to the other evolutionary algorithms and engineering design problems in its performance, quality and efficiency.

Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model

Wind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porté-Agel model). Two different parametrizations of the evolutionary methodology are implemented, depending on if a baseline layout is considered or not. The proposed scheme is applied to two real wind farms, Horns Rev I (Denmark) and Princess Amalia (the Netherlands), and two different turbine models, V80-2MW and NREL-5MW. For comparison purposes, these four study cases are also optimized under the traditionally used top-hat wake model (Jensen model). A systematic overestimation of the wake losses by the Jensen model is confirmed herein. This allows it to attain bigger power output increases with respect to the baseline layouts (between 0.72% and 1.91%) compared to the solutions attained through the more realistic Gaussian model (0.24–0.95%). The proposed methodology is shown to outperform other recently developed layout optimization methods. Moreover, the electricity cable length needed to interconnect the turbines decreases up to 28.6% compared to the baseline layouts.

Quantitative analyses of the metaphase-to-anaphase transition reveal differential kinetic regulation for securin and cyclin B1

Separation of sister chromatids is a drastic and irreversible step in the cell cycle. The key biochemistry behind this event is the proteolysis mediated by the ubiquitin ligase called the anaphase promoting complex, or APC/C. Securin and cyclin B1 are the two established substrates for APC/C whose degradation releases separase and inactivates cyclin B1-dependent kinase 1 (cdk1), respectively, at the metaphase-to-anaphase transition. In this study, we have combined biochemical quantifications with mathematical simulations to characterize the kinetic regulation of securin and cyclin B1, in the cytoplasmic and chromosomal compartments, and found that they are differentially distributed and degraded with different rates. Modeling their interaction with separase predicted that activation timing of separase well coincides with the decline of securin-separase concentration in the cytoplasm. Notably, it also coincides with the peak of cyclin B1-separase level on chromosomes, which appeared crucial to coordinate the timing for separase activation and cdk1 inhibition. We have also conducted phosphoproteomic analysis and identified Ki67 as a chromosomal cdk1 substrate whose dephosphorylation is facilitated by cyclin B1-separase interaction in anaphase.

Mobile robot wall-following control using a fuzzy cerebellar model articulation controller with group-based strategy bacterial foraging optimization

In this study, a fuzzy cerebellar model articulation controller based on group-based strategy bacterial foraging optimization is proposed for mobile robot wall-following control. In fuzzy cerebellar model articulation controller, the inputs are the distance between the sonar and the wall, and the outputs are the angular velocity of two wheels. The proposed group-based strategy bacterial foraging optimization learning algorithm is used to adjust the parameters of fuzzy cerebellar model articulation controller model. The proposed group-based strategy bacterial foraging optimization has the advantages of global search, evolutionary strategies, and group evolution to speed up the convergent rate. A new fitness function is defined to evaluate the performance of mobile robot wall-following control. The fitness function includes four assessment factors which are defined as follows: (1) maintaining safe distance between the mobile robot and the wall, (2) ensuring successfully running a cycle, (3) avoiding mobile robot collisions, and (4) mobile robot running at a maximum speed. The experimental results show that the proposed group-based strategy bacterial foraging optimization obtains a better wall-following control than other methods in unknown environments.

Optimizing micropattern geometries for cell shape and migration with genetic algorithms

Adhesive micropatterns have become a standard tool to control cell shape and function in cell culture.

Understanding Networks of Computing Chemical Droplet Neurons Based on Information Flow

In this paper, we present general methods that can be used to explore the information processing potential of a medium composed of oscillating (self-exciting) droplets. Networks of Belousov–Zhabotinsky (BZ) droplets seem especially interesting as chemical reaction-diffusion computers because their time evolution is qualitatively similar to neural network activity. Moreover, such networks can be self-generated in microfluidic reactors. However, it is hard to track and to understand the function performed by a medium composed of droplets due to its complex dynamics. Corresponding to recurrent neural networks, the flow of excitations in a network of droplets is not limited to a single direction and spreads throughout the whole medium. In this work, we analyze the operation performed by droplet systems by monitoring the information flow. This is achieved by measuring mutual information and time delayed mutual information of the discretized time evolution of individual droplets. To link the model with reality, we use experimental results to estimate the parameters of droplet interactions. We exemplarily investigate an evolutionary generated droplet structure that operates as a NOR gate. The presented methods can be applied to networks composed of at least hundreds of droplets.

A Class Association Rule Based Classifier Using Probability Density Functions for Intrusion Detection Systems

As the number of computer systems connected to the Internet is increasing exponentially, the computer security has become a crucial problem, and many techniques for Intrusion detection have been proposed to detect network attacks efficiently. On the other hand, data mining algorithms based on Genetic Network Programming (GNP) have been proposed and applied to Intrusion detection recently. GNP is a graph-based evolutionary algorithm and can extract many important class association rules by making use of the distinguished representation ability of the graph structure. In this paper, probabilistic classification algorithms based on multi-dimensional probability distribution are proposed and combined with conventional class association rule mining of GNP, and applied to network intrusion detection for the performance evaluation. The proposed classification algorithms are based on 1) one-dimensional probability density functions and 2) a two-dimensional joint probability density function. These functions represent the distribution of normal and intrusion accesses and efficiently classify a new access data into normal, known intrusion or even unknown intrusion. The simulations using KDD99Cup database from MIT Lincoln Laboratory show some advantages of the proposed algorithms over the conventional mean and standard deviation-based method.

On disciplinary fragmentation and scientific progress

Why are some scientific disciplines, such as sociology and psychology, more fragmented into conflicting schools of thought than other fields, such as physics and biology? Furthermore, why does high fragmentation tend to coincide with limited scientific progress? We analyzed a formal model where scientists seek to identify the correct answer to a research question. Each scientist is influenced by three forces: (i) signals received from the correct answer to the question; (ii) peer influence; and (iii) noise. We observed the emergence of different macroscopic patterns of collective exploration, and studied how the three forces affect the degree to which disciplines fall apart into divergent fragments, or so-called “schools of thought”. We conducted two simulation experiments where we tested (A) whether the three forces foster or hamper progress, and (B) whether disciplinary fragmentation causally affects scientific progress and vice versa. We found that fragmentation critically limits scientific progress. Strikingly, there is no effect in the opposite causal direction. What is more, our results shows that at the heart of the mechanisms driving scientific progress we find (i) social interactions, and (ii) peer disagreement. In fact, fragmentation is increased and progress limited if the simulated scientists are open to influence only by peers with very similar views, or when within-school diversity is lost. Finally, disciplines where the scientists received strong signals from the correct answer were less fragmented and experienced faster progress. We discuss model’s implications for the design of social institutions fostering interdisciplinarity and participation in science.