Multi-armed spirals and multi-pairs antispirals in spatial rock–paper–scissors games

We study the formation of multi-armed spirals and multi-pairs antispirals in spatial rock–paper–scissors games with mobile individuals. We discover a set of seed distributions of species, which is able to produce multi-armed spirals and multi-pairs antispirals with a finite number of arms and pairs based on stochastic processes. The joint spiral waves are also predicted by a theoretical model based on partial differential equations associated with specific initial conditions. The spatial entropy of patterns is introduced to differentiate the multi-armed spirals and multi-pairs antispirals. For the given mobility, the spatial entropy of multi-armed spirals is higher than that of single armed spirals. The stability of the waves is explored with respect to individual mobility. Particularly, we find that both two armed spirals and one pair antispirals transform to the single armed spirals. Furthermore, multi-armed spirals and multi-pairs antispirals are relatively stable for intermediate mobility. The joint spirals with lower numbers of arms and pairs are relatively more stable than those with higher numbers of arms and pairs. In addition, comparing to large amount of previous work, we employ the no flux boundary conditions which enables quantitative studies of pattern formation and stability in the system of stochastic interactions in the absence of excitable media.     

Evolutionary model of the growth and size of firms

The key idea of this model is that firms are the result of an evolutionary process. Based on demand and supply considerations the evolutionary model presented here derives explicitly Gibrat’s law of proportionate effects as the result of the competition between products. Applying a preferential attachment mechanism for firms, the theory allows to establish the size distribution of products and firms. Also established are the growth rate and price distribution of consumer goods. Taking into account the characteristic property of human activities to occur in bursts, the model allows also an explanation of the size–variance relationship of the growth rate distribution of products and firms. Further the product life cycle, the learning (experience) curve and the market size in terms of the mean number of firms that can survive in a market are derived. The model also suggests the existence of an invariant of a market as the ratio of total profit to total revenue. The relationship between a neo-classic and an evolutionary view of a market is discussed. The comparison with empirical investigations suggests that the theory is able to describe the main stylized facts concerning the size and growth of firms.     

Quantum strategies win in a defector-dominated population

Quantum strategies are introduced into evolutionary games. The agents using quantum strategies are regarded as invaders, whose fraction generally is 1% of a population, in contrast to the 50% of the population that are defectors. In this paper, the evolution of strategies on networks is investigated in a defector-dominated population, when three networks (square lattice, Newman–Watts small-world network, and scale-free network) are constructed and three games (Prisoners’ Dilemma, Snowdrift, and Stag-Hunt) are employed. As far as these three games are concerned, the results show that quantum strategies can always invade the population successfully. Comparing the three networks, we find that the square lattice is most easily invaded by agents that adopt quantum strategies. However, a scale-free network can be invaded by agents adopting quantum strategies only if a hub is occupied by an agent with a quantum strategy or if the fraction of agents with quantum strategies in the population is significant.     

Evolutionary game on a stochastic growth network

In some real complex systems the structures are difficult to map or changing over time. To explore the evolution of strategies on these complex systems, it is not realistic enough to specify their structures or topological properties in advance. In this paper, we address the evolutionary game on a stochastic growth network adopting the prisoner’s dilemma game. We introduce a growing rate q$q$ to control the ratio of network growth to strategy evolution. A large q$q$ denotes that the network grows faster than strategy evolution. Simulation results show that a fast growing rate is helpful to promote the average payoffs of both cooperators and defectors. Moreover, this parameter also significantly influences the cooperation frequency on the resulting networks. The coexisting mechanisms in this paper may provide a beneficial insight for understanding the emergence of complex topological structures and game behaviors in numerous real systems.     

Age-related vitality of players promotes the evolution of cooperation in the spatial prisoner’s dilemma game

Within most real biological or social systems, age plays an important role in the vitality of individuals. For instance, individuals become more and more weak with age, while young individuals may prosper even under poor living conditions. In this paper, we find that aging as a coevolutionary process promotes cooperation more effectively than quenched heterogeneous distributions of age. By studying the average age of defectors and cooperators along the boundaries separating the domains of the two strategies, we reveal the significance of coevolutionary rules for the successful evolution of cooperation amongst selfish and unrelated individuals.     

The emergence of leadership in social networks

We study a networked version of the minority game in which agents can choose to follow the choices made by a neighbouring agent in a social network. We show that for a wide variety of networks a leadership structure always emerges, with most agents following the choice made by a few agents. We find a suitable parameterisation which highlights the universal aspects of the behaviour and which also indicates where results depend on the type of social network.     

Word diversity can accelerate consensus in naming game

In this paper, we introduce word diversity that reflects the inhomogeneity of words in a communication into the naming game. Diversity is realized by assigning a weight factor to each word. The weight is determined by three different distributions (uniform, exponential, and power-law distributions). During the communication, the probability that a word is selected from speaker's memory depends on the introduced word diversity. Interestingly, we find that the word diversity following three different distributions can remarkably promote the final convergency, which is of high importance in the self-organized system. In particular, for all the ranges of amplitude of distribution, the power-law distribution enables the fastest consensus, while uniform distribution gives the slowest consensus. We provide an explanation of this effect based on both the number of different names and the number of total names, and find that a wide spread of names induced by the segregation of words is the main promotion factor. Other quantities, including the evolution of the averaging success rate of negotiation and the scaling behavior of consensus time, are also studied. These results are helpful for better understanding the dynamics of the naming game with word diversity.     

A Single Component Mutation Evolutionary Programming

In this paper, a novel evolutionary programming is proposed for solving the upper and lower bound optimization problems as well as the linear constrained optimization problems. There are two characteristics of the algorithm: first, only one component of the current solution is mutated in each iteration; second, it can solve the linear constrained optimization problems directly without converting it into unconstrained problems. By solving two kinds of the optimization problems, the algorithm can not only effectively find optimal or close to optimal solutions but also reduce the number of function evolutions compared with the other heuristic algorithms.     

A self-adaptive global best harmony search algorithm for continuous optimization problems

This paper presents a self-adaptive global best harmony search (SGHS) algorithm for solving continuous optimization problems. In the proposed SGHS algorithm, a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies. The harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) are dynamically adapted by the learning mechanisms proposed. The distance bandwidth (BW) is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature. The computational results show that the proposed SGHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants.     

Mining Pareto-optimal modules for delayed product differentiation

This paper presents a framework for finding optimal modules in a delayed product differentiation scenario. Historical product sales data is utilized to estimate demand probability and customer preferences. Then this information is used by a multiple-objective optimization model to form modules. An evolutionary computation approach is applied to solve the optimization model and find the Pareto-optimal solutions. An industrial case study illustrates the ideas presented in the paper. The mean number of assembly operations and expected pre-assembly costs are the two competing objectives that are optimized in the case study. The mean number of assembly operations can be significantly reduced while incurring relatively small increases in the expected pre-assembly cost.