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In evolutionary robotics (ER), robotic control systems are subject to a developmental process inspired by natural evolution. The purpose of this paper is to utilize a control system representation based on finite state machines (FSMs) to build a decentralized online‐evolutionary framework for swarms of mobile robots.

A new recombination operator for multi‐parental generation of offspring is presented and a known mutation operator is extended to harden parts of genotypes involved in good behavior, thus narrowing down the dimensions of the search space. A storage called memory genome for archiving the best genomes of every robot introduces a decentralized elitist strategy. These operators are studied in a factorial set of experiments by evolving two different benchmark behaviors such as collision avoidance and gate passing on a simulated swarm of robots. A comparison with a related approach is provided.

The framework is capable of robustly evolving the benchmark behaviors. The memory genome and the number of parents for reproduction highly influence the quality of the results; the recombination operator leads to an improvement in certain parameter combinations only.

Future studies should focus on further improving mutation and recombination. Generality statements should be made by studying more behaviors and there is a need for experimental studies with real robots.

The design of decentralized ER frameworks is improved.

The framework is robust and has the advantage that the resulting controllers are easier to analyze than in approaches based on artificial neural networks. The findings suggest improvements in the general design of decentralized ER frameworks.

This research paper reports a novel genetic algorithm (GA)‐based approach for reconfiguration of radial distribution networks for real loss minimization and power quality improvement.

A fuzzy controlled GA has been used for efficient reconfiguration of radial distribution systems for loss minimization and power quality improvement. The special features of the proposed algorithm are: an improved chromosome coding/decoding for network representation so as to preserve the radial property without islanding any load after reconfiguration and an efficient convergence characteristics attributed to fuzzy controlled mutation.

The proposed network reconfiguration algorithm is very much effective in arriving at the global optimal solution (minimum loss network structure) because of efficient search of the solution space. Also, no invalid chromosomes are generated in the genetic evolution because of appropriate coding/decoding. The algorithm is found to be very much suitable for real time implementations.

This research paper provides the power distribution engineers with a computationally efficient approach for optimal operation of distribution systems.

The algorithm proposed in this paper is computationally much faster compared to most of the present day mathematical programming approaches for distribution system operation. This makes it very much attractive for online implementations in any radial distribution network.

This paper has proposed a novel chromosome coding/decoding technique for radial distribution system and a fuzzy logic‐based mutation probability controller for efficient search of global solution space to be used in GA‐based optimal operation of radial distribution systems.

The purpose of this paper is to seek an efficient method to tackle the energy provision problem for smart grid with sparse constraints and distributed energy and storage devices.

A complex smart grid is first studied, in which sparse constraints and the complex make-up of different energy consumption due to the integration of distributed energy and storage devices and the emergence of multisellers are discussed. Then, a real-time pricing scheme is formulated to tackle the demand response based on sparse bilevel programming. And then, a bilevel genetic algorithm (BGA) is further designed. Finally, simulations are conducted to evaluate the performance of the proposed approach.

The considered situation is widespread in practice, and meanwhile, the other cases including traditional model without the sparse constraints can be seen as its extensions. The BGA based on sparse bilevel programming has advantages of “no need of convexity of the model.” Moreover, it is feasible without the need to disclose the private information to others; therefore, privacies are protected and system scalability is kept. Simulation results validate the proposed approach has good performance in maximizing social welfare and balancing system energy distribution.

In this paper, the authors consider the sparse constraints due to the fact that each user can only choose limited utility companies per time slot. In reality, there exist some other sparse cases, which deserve further study in the future.

To the best of the authors’ knowledge, this is one of the very first studies to address pricing problems for the smart grid with consideration of sparse constraints and integration of distributed energy and storage devices.

Leakage current is one of the factors, which can contribute towards degradation of surge arresters. Thus, the purpose of this paper is to study on leakage current within surge arresters and improvement on their design.

In this work, a three-dimensional model geometry of 11 kV zinc oxide surge arrester was designed in finite element analysis and was applied to calculate the leakage current under normal operating condition and being verified with measurement results. The optimisation methods were used to improve the arrester design by minimising the leakage current across the arrester using imperialist competitive algorithm (ICA) and gravitational search algorithm (GSA).

The arrester design in reducing leakage current was successfully optimised by varying the glass permittivity, silicone rubber permittivity and the width of the ground terminal of the surge arrester. It was found that the surge arrester design obtained using ICA has lower leakage current than GSA and the original design of the surge arrester.

The comparison between measurement and simulation enables factors that affect the mechanism of leakage current in surge arresters to be identified and provides the ideal design of arrester.

Surge arrester design was optimised by ICA and GSA, which has never been applied in past works in designing surge arrester with minimum leakage current.

Team social capital (TSC) has been attracting increasing research attention aiming to explore team effectiveness through within- and cross-team resource conduits. This study bridges two disconnected theories – TSC and evolutionary theory – to examine gaming clans and analyzes mechanisms of the clans' TSC building from an evolutionary perspective.

This research draws longitudinal data from a sample of gaming teams (N = 1,267) from anonymized player data from the game World of Tanks spanning 32 months. The authors explored teams' evolutionary patterns using hidden Markov models and applied longitudinal multilevel modeling to test hypotheses.

The results showed that teams of different sizes and levels of evolutionary fitness vary in team closure and bridging social capital. The authors also found that larger teams are more effective than smaller ones. The positive association between team-bridging social capital and effectiveness is more substantial for smaller teams.

This research advances the theoretical development of TSC by including the constructs of teams' evolutionary status when analyzing strategic social capital building. Adding to existing literature studying the outcome of TSC, this research also found a moderating effect of team size between TSC and effectiveness. Finally, this study also contributes to a longitudinal view of TSC and found significant evolutionary patterns of teams' membership, TSC, and effectiveness.

A new design framework for crossover operator is proposed based on the commonality concept. In the reproduction process the resulting hybrid crossover operator includes a local search scheme aiming to improve the genetic characteristics of the offspring. Commonality suggests that search should be driven in the neighbourhood of parents, and local optimisers can drive this search. The ranking of the offspring candidates is based on a local fitness function using approximations and appropriated heuristics linked to the structural optimisation problem. The goal of this approach is to identify and preserve the common schema of the two parents responsible for their high‐observed fitness. The proposed hybrid crossover operator is embedded into a genetic algorithm supported by an elitist strategy and its performance is compared with the parametrised uniform crossover.

This paper aims at contributing to a better knowledge of organizations' nature, physiology and pathologies, in order to improve their fitness for purpose. The mechanistic view of organizations has in fact delayed that. Systems thinking is needed to bring average organizational fitness to the levels needed by a global and closely interconnected world.

The paper is a synthesis of the author's experience as manager, consultant and teacher. By thinking back to the last 30 years of history of managing for quality and excellence, failures and successes, the causes of delay and even regression are explored. Borrowing from the systems view of organizations, a parallel is made between history of human beings' and organizations' healthcare.

Knowledge of the factors that make organizations fit for their purpose is still scarce, absolutely unfit for the challenges of an uncertain future. That is particularly true for those large organizations that govern globalization. Risks for humanity increase. It is no longer time to fiddle with management fads or panaceas for all diseases. It is time to use the modern approaches to complexity that systems thinking offers, overcoming the resistance of traditional thinking. Analytical thinking alone, in fact, may lead to squeeze the planet resources dry, neglecting the risks of long‐term negative impacts.

Conformism in managing for quality is still high. Rare are the papers that discuss the evolution of TQM/excellence models towards systemic models, where the system is socio‐cultural and the model covers doing the right things, not just doing things right.

Tourism areas are challenged to become adaptive areas in the context of a dynamic networked society and globalizing economy. The purpose of this paper is to contribute to an enhanced understanding and conceptualization of adaptive tourism areas by drawing attention to “fitness landscapes,” a metaphor that is used in complexity theories to visualize development trajectories of adaptive systems.

Fitness landscapes, and its underlying theories, are useful to conceptualize tourism area development as a stepwise movement through a dynamic landscape with peaks and valleys. Doing so allows us to highlight why adaptation is a crucial property for tourism areas that are embedded in dynamic contexts and offers a frame of thought for how tourism areas can be managed.

The article raises awareness about and draws attention to a set of factors and conditions that support tourism planners and managers in enhancing the capacity of tourism areas to adaptively respond to changing circumstances.

Introducing fitness landscapes contribute to the discussion on adaptive capacity building – a topic that contributes to managing uncertain futures and is likely to gain importance in the dynamic society. Moreover, it helps as well as stimulates tourism scholars to further develop this topic. Finally, it helps tourism planners to build adaptive capacity in practice.

The purpose of this paper is to analyze whether the fitness industry in Belgium is financially viable in its position as a growing commercial player within the framework of the European sport model where non-profit and public sport providers still have a strong impact.

The authors evaluate the financial performance of the Belgian fitness industry using a time-trend analysis applying a cross-sectional research design for the years 2002 through 2007.

The analysis shows that the Belgian fitness industry is not able to generate positive income figures despite large increases in sales revenues. In particular fitness chains generally accumulate losses. However, the Belgian fitness industry pursues an active investment policy resulting in high noncash expenses in depreciations negatively influencing accounting profit numbers. The operating cash flow generated by the Belgian fitness industry is, nevertheless, largely positive. Although no immediate liquidity problem exists, the fitness industry needs to improve its profitability in the long run in order to stay in business.

This study can be a starting point for further and more in depth financial performance evaluations of commercial actors in the field of sport. Differences and similarities between European countries should be investigated in order to generalize the findings.

The conclusions could support regulators in policy decisions and business managers in strategic decisions relying on financial information in order to pilot their organization.

Analyzing the financial performance of a sport industry at a national scale is challenging. However, this kind of analysis is not frequently performed for commercial sport providers such as the fitness industry. This is precisely where this paper wants to contribute.

A numerical method for shape optimisation in forging is presented. The goal of the optimisation is to eliminate work‐piece defects that may arise during the forging process. A two‐dimensional finite element code has been developed for the simulation of the mechanical process. The material is incompressible and it follows the Norton‐Hoff law. To deal with contact constraint the velocity projection algorithm is used. The optimisation process is conducted using a genetic algorithm supported by an elitist strategy. A new genetic operator called adaptive mutation has been developed to increase the efficiency of the search. The developed scheme is used to design optimal preform shapes for several axisymmetric examples. Continuous and discrete design variables are considered. The objective function of the optimisation problem is associated with the quality of the final product. Comparing the obtained optimal results with the literature validates the proposed optimisation method.