Diversity Improves Teamwork: Optimising Teams using a Genetic Algorithm

The Law of the Best or the Law of Averages? Modeling and Evidence from the Digital Game Industry

Introduction: Virtual teams, especially project teams, increasingly rely on computer-mediated communication for interaction when collaborating and completing their tasks. Team members represent various cultures, occupations, and industries. Virtual teams often use English as a business lingua franca in communication. This study investigates critical factors related to virtual project teams that influence computer-mediated communication. Research questions: 1. How is diversity in video meetings experienced by the members of multidisciplinary and multicultural project teams? 2. Do differences in team members' occupational or industrial backgrounds show in their opinions on video meetings? Literature review: The effectiveness of communication has been identified as playing a critical role in the success of virtual projects. Diversity, whether disciplinary or cultural, enriches teamwork by bringing different viewpoints to discussions. On the other hand, diversity can also set some challenges for communicating these viewpoints. Research methodology: Mixed methods were used to analyze data obtained from 104 responses to an online survey. Spearman's correlation coefficient and Kruskall-Wallis nonparametric tests were used for statistical analysis, and open comments were analyzed using thematic content analysis. Results and conclusion: The findings show that linguistic, cultural, and occupational diversity facilitates communication in virtual project team meetings. In addition, applying appropriate features of video meeting tools in different stages of project teamwork leads to better communication in virtual teams. A high level of English proficiency is not required, but clear communication rules are essential. In addition, some occupational or industry-specific differences in opinions on communication could be identified.

Changes in Web infrastructure have allowed ITexts to become a vehicle for transdisciplinary Web-scale collaboration so that large-scale teams can create new knowledge despite differences in team members’ disciplinary training, geographic location, and levels of expertise. In this article, the authors define Web-scale collaboration and illustrate the need for transdisciplinary approaches to problem solving. Then they introduce heuristics for creating and evaluating such transdisciplinary, collaborative Web-scale ITexts, drawing on examples generated at a workshop sponsored by the National Science Foundation that was held at Rensselaer Polytechnic Institute in April 2010.

The New Jersey Kids Study (NJKS) is a transdisciplinary statewide initiative to understand influences on child health, development, and disease. We conducted a mixed-methods study of project planning teams to investigate team effectiveness and relationships between team dynamics and quality of deliverables. Ten theme-based working groups (WGs) (e.g., Neurodevelopment, Nutrition) informed protocol development and submitted final reports. WG members (n = 79, 75%) completed questionnaires including de-identified demographic and professional information and a modified TeamSTEPPS Team Assessment Questionnaire (TAQ). Reviewers independently evaluated final reports using a standardized tool. We analyzed questionnaire results and final report assessments using linear regression and performed constant comparative qualitative analysis to identify central themes. WG-level factors associated with greater team effectiveness included proportion of full professors (β = 31.24, 95% CI 27.65-34.82), team size (β = 0.81, 95% CI 0.70-0.92), and percent dedicated research effort (β = 0.11, 95% CI 0.09-0.13); age distribution (β = -2.67, 95% CI -3.00 to -2.38) and diversity of school affiliations (β = -33.32, 95% CI -36.84 to -29.80) were inversely associated with team effectiveness. No factors were associated with final report assessments. Perceptions of overall initiative leadership were associated with expressed enthusiasm for future NJKS participation. Qualitative analyses of final reports yielded four themes related to team science practices: organization and process, collaboration, task delegation, and decision-making patterns. We identified several correlates of team effectiveness in a team science initiative's early planning phase. Extra effort may be needed to bridge differences in team members' backgrounds to enhance the effectiveness of diverse teams. This work also highlights leadership as an important component in future investigator engagement.

Design for emerging technologies such as ubiquitous computing, calm technology and Grid computing requires knowledge and expertise from different disciplines. Such knowledge helps to improve our individual information processing resources and thus overcome our limitations. However, integrating design knowledge and expertise of different team members coming from different disciplines is a complex task. Today, we are facing two problems. Firstly, how to enable the multidisciplinary design team to solve problems given the need to build common ground amongst team members? Secondly, how to structure the information which is held about projects so that issues can be progressed and resolved, in particular those arising as a result of the diversity of team members. In this paper, we propose an approach based on common ground theory and IBIS (Issue Based Information System) to address issues encountered by the multidisciplinary team and to enable the decision making process. We also provide a means of evaluating the approach by using statistical analysis.

This study investigates whether subjective communication from team members to a manager responsible for allocating performance-based bonuses increases team performance and whether the efficacy of such communication is reduced in heterogeneous teams. We draw on both economic and behavioral theories to predict that communication content, even though subjective, provides information that enables the manager to allocate bonuses so as to enhance the relation between individuals' contributions and rewards, thereby increasing individuals' effort and team performance. However, we also predict that the positive effect of team member subjective communication is more muted when team members' abilities are heterogeneous compared to homogeneous. We test these predictions via an experiment. Consistent with our predictions, team member subjective communication has a positive effect on team performance, and the positive effect is more muted for heterogeneous teams. Results of our study contribute to both theory and practice by enhancing our understanding of the role of subjective communication from team members to team managers in motivating effort in teams and, particularly, how its efficacy is affected by team composition. Data Availability: Data are available from the authors upon request.

The term "Artificial Human Optimization" was first created by the corresponding author of this paper in December 2016, when he published a paper titled "Entrepreneur : Artificial Human Optimization" in Transactions on Machine Learning and Artificial Intelligence (TMLAI) Volume 4, No 6 in December 2016. (December 2016). The Artificial Human Optimization Field is described as the collection of all optimization methods suggested based on Artificial Humans, according to a paper released in 2016. In the real world, we (Humans) are responsible for resolving issues. Artificial Humans imitate real Humans in the search space and solve optimization issues in the same way. The basic entities in the solution space of Particle Swarm Optimization (PSO) are Artificial Birds, whereas the basic entities in the search space of Artificial Human Optimization are Artificial Humans. Each Artificial Human is associated with a certain location in the solution space. The following are ten strategies for Artificial Human Optimization :“Human Bhagavad Gita Particle Swarm Optimization (HBGPSO)”, “Human Poverty Particle Swarm Optimization (HPPSO)”, “Human Dedication Particle Swarm Optimization (HuDePSO)”, “Human Selection Particle Swarm Optimization (HuSePSO)”, “Human Safety Particle Swarm Optimization (HuSaPSO)”, “Human Kindness Particle Swarm Optimization (HKPSO)”, “Human Relaxation Particle Swarm Optimization (HRPSO)”, “Multiple Strategy Human Particle Swarm Optimization (MSHPSO)”, “Human Thinking Particle Swarm Optimization (HTPSO)”, “Human Disease Particle Swarm Optimization (HDPSO)”. The results of applying ten Artificial Human Optimization methods to various benchmark functions are presented in this paper.&nbsp

Many realistic problem domains are composed of heterogeneous tasks distributed in a physical environment. Even though the distribution of skills among the members of a heterogeneous team has a significant influence on its effectiveness, little is known about how to design effective heterogeneous teams. In this paper, we develop a graph-search approach to tackle this team design problem in the context of multiagent routing, a generalizable domain in which heterogeneous, randomly located tasks must be completed in overall minimum time (or make span) given an a priori distribution of their heterogeneity, a fixed team size, and a limited budget. First, we develop complete and optimal search algorithms. Second, we show that dominance-based pruning significantly increases the size of problems that can be solved optimally. Third, we introduce an anytime algorithm called TD-BR that uses beam search with restarts in order to scale up to much larger problems. We evaluate our algorithms empirically in two ways: first, we predict the performance of the teams using a team performance metric called task coverage, and show that our algorithms produce high coverage teams, second, we test a subset of these teams in simulation by allocating the teams to various task sets and measuring their make span. We show that our teams perform well when compared to an ideal homogeneous team, and outperform heterogeneous teams created by other methods. Our main contributions are thus new algorithmic tools for designers of heterogeneous teams in robotics and other domains where modular construction and refitting of robots is possible.

Improving team interactions in engineering to model gender inclusivity has been at the forefront of many initiatives in both academia and industry. However, there has been limited evidence on the impact of gender-diverse teams on psychological safety. This is important because psychological safety has been shown to be a key facet for the development of innovative ideas, and has also been shown to be a cornerstone of effective teamwork. But how does the gender diversity of a team impact the development of psychological safety? The current study was developed to explore just this through an empirical study with 38 engineering design student teams over the course of an 8-week design project. These teams were designed to be half heterogeneous (either half-male and half-female, or majority male) or other half homogeneous (all male). We captured psychological safety at five time points between the homogenous and heterogenous teams and also explored individual dichotomous (peer-review) ratings of psychological safety at the end of the project. Results indicated that there was no difference in psychological safety between gender homogenous and heterogenous teams. However, females perceived themselves as more psychologically safe with other female team members compared to their ratings of male team members. Females also perceived themselves to be less psychologically safe with male team members compared to male ratings of female team members, indicating a discrepancy in perceptions between genders. These results point to the need to further explore the role of minoritized groups in psychological safety research and to explore how this effect presents itself (or is covered up) at the team level.

Purpose:We present a particle swarm optimization (PSO)‐based 4D IMRT planning technique designed for dynamic MLC tracking delivery to lung tumors. The key idea is to utilize the temporal dimension as an additional degree of freedom rather than a constraint in order to achieve improved sparing of organs at risk (OARs).Methods:The target and normal structures were manually contoured on each of the ten phases of a 4DCT scan acquired from a lung SBRT patient who exhibited 1.5cm tumor motion despite the use of abdominal compression. Corresponding ten IMRT plans were generated using the Eclipse treatment planning system. These plans served as initial guess solutions for the PSO algorithm. Fluence weights were optimized over the entire solution space i.e., 10 phases × 12 beams × 166 control points. The size of the solution space motivated our choice of PSO, which is a highly parallelizable stochastic global optimization technique that is well‐suited for such large problems. A summed fluence map was created using an in‐house B‐spline deformable image registration. Each plan was compared with a corresponding, internal target volume (ITV)‐based IMRT plan.Results:The PSO 4D IMRT plan yielded comparable PTV coverage and significantly higher dose—sparing for parallel and serial OARs compared to the ITV‐based plan. The dose‐sparing achieved via PSO‐4DIMRT was: lung Dmean = 28%; lung V20 = 90%; spinal cord Dmax = 23%; esophagus Dmax = 31%; heart Dmax = 51%; heart Dmean = 64%.Conclusion:Truly 4D IMRT that uses the temporal dimension as an additional degree of freedom can achieve significant dose sparing of serial and parallel OARs. Given the large solution space, PSO represents an attractive, parallelizable tool to achieve globally optimal solutions for such problems. This work was supported through funding from the National Institutes of Health and Varian Medical Systems.Amit Sawant has research funding from Varian Medical Systems, VisionRT Ltd. and Elekta

This article examines the dynamics of entrepreneurial teams as they evolve through the different stages of a spin–out process. Using a unique, hand–collected set of data covering all team members in 10 cases, an in–depth analysis of the heterogeneity of team members’ experience and perception of the strategic orientation needed to attain different milestones in the spin–out process was performed. Our findings suggest that teams evolve over time and change in composition, and therefore, they cannot be studied as immutable entities. At the start of the venture formation, we introduced a new team role, the privileged witness, potentially specific for spinouts. Analysis of the teams indicates that the team's heterogeneity changes as it evolves through the different stages of the spin–out process. In particular, we found that new team members brought in different kinds of experience; however, they did not introduce a different view on doing business from the initial team members.

Purpose : This study explores how diverse team compositions influence table tennis skill development in college students during a Sport Education (SE) season, considering gender and initial skill-level differences. Method : A quasiexperimental design included 64 college students, evenly split into control and experimental groups based on gender. Control group had heterogeneous teams, while experimental group had both heterogeneous and homogeneous teams. The 16-week SE season assessed backhand and forehand blocking skill improvement through two weekly sessions (45 min each). Results : Both groups exhibited skill development, with no significant gender differences. However, the experimental group demonstrated more comparable skill development across different skill levels compared with control group. Conclusion : The study underscores the positive impact of a design combining homogeneous and heterogeneous teams in SE on college students’ table tennis skill development. Educators are advised to consider context-specific factors, including sociocultural influences and sport skill complexity, when structuring team compositions within SE.

Joint Impact of Interdependence and Group Diversity on Innovation

Allocation for earthquake emergency shelters is a complicated geographic optimization problem because it involves multiple sites, strict constraints, and discrete feasible domain. Huge solution space makes the problem computationally intractable. Traditional brute-force methods can obtain exact optimal solutions. However, it is not sophisticated enough to solve the complex optimization problem with reasonable time especially in high-dimensional solution space. Artificial intelligent algorithms hold the promise of improving the effectiveness of location search. This article proposes a modified particle swarm optimization (PSO) algorithm to deal with the allocation problem of earthquake emergency shelter. A new discrete PSO and the feasibility-based rule are incorporated according to the discrete solution space and strict constraints. In addition, for enhancing search capability, simulated annealing (SA) algorithm is employed to escape from local optima. The modified algorithm has been applied to the allocation of earthquake emergency shelters in the Zhuguang Block of Guangzhou City, China. The experiments have shown that the algorithm can identify the number and locations of emergency shelters. The modified PSO algorithm shows a better performance than other hybrid algorithms presented in the article, and is an effective approach for the allocation problem of earthquake emergency shelters.

In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability and overshoot. Therefore, finding the optimal tuning parameters is of utmost importance. To address this optimization problem, we propose the utilization of two popular metaheuristic algorithms, GA and PSO. These algorithms are known for their ability to efficiently search through large solution spaces and find near-optimal solutions. By applying these algorithms to the PI controller tuning problem, we aim to determine which technique yields better results in terms of stability and overshoot tuning. In our comparative study, we provide a detailed explanation of both GA and PSO algorithms, focusing on their working principles and mathematical formulations. We also describe how these algorithms can be applied to the PI controller tuning problem. Furthermore, we highlight the key differences between GA and PSO, shedding light on their strengths and weaknesses. To assess the performance of GA and PSO, we conduct several experiments using different benchmark functions and step response models. We measure the stability and overshoot metrics for various parameter settings obtained through GA and PSO. By thoroughly analyzing the obtained results, we draw meaningful conclusions regarding the effectiveness of each technique. Our findings demonstrate that both GA and PSO exhibit promising results in optimizing PI controller tuning. These observations provide valuable insights and guidelines for choosing the appropriate algorithm based on specific control requirements. In conclusion, this comparative study is thought to contribute to the field of control systems engineering by offering a comprehensive analysis of GA and PSO techniques in the context of PI controller tuning. By highlighting their strengths and weaknesses, it is aimed to provide researchers and practitioners with valuable information for making informed decisions when optimizing control parameters for stability and overshoot reduction purposes.