Cooperative Evolution

We study the evolution of cooperation in public goods games on the square lattice, focusing on the co-player learning mechanism based on the preferential selection that are brought about by wealthy information of groups where participants collect and search for potential imitators from those groups. We find that co-player learning mechanism based on the choice of weighted group can lead to the promotion of public cooperation by means of the information of wealthy groups that is obtained by participants, and after that the partial choice of public goods groups is enhanced with the tunable preferential parameter. Our results highlight that the learning interactions is not solely confined to the restricted connection among players, but co-players of wealthy groups have the opportunity to be as a role model in the promotion of cooperative evolution. Moreover, we also find the size of learning affects the choice of distant players, cooperators (defectors) having more paths to exploit the phalanx of opponents to survive when the value of preferential parameter is small. Besides, the extinction thresholds of cooperators and defectors for different values of noise are also investigated.

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Shift Scheduling of Short Time Workers in Large-Scale Home Improvement Center by using Cooperative Evolution

MENDEL ◽

10.13164/mendel.2017.1.021 ◽

2017 ◽

Vol 23 (1) ◽

pp. 21-28

Author(s):

Makoto Ohki

Keyword(s):

Large Scale ◽

Shift Scheduling ◽

Time Zone ◽

Cooperative Evolution ◽

Shift Schedule ◽

Home Improvement ◽

Mutation Operators ◽

Break Time ◽

Working Day ◽

Short Time

There are a lot of large-scale Home Improvement Center (HIC) in Japan. In the large-scale HIC,about hundred short time workers are registered. And about forty workers are working every day. A managercreates a monthly shift schedule. The manager selects the workers required for each working day, assigns theworking time and break time for each worker and also work placement. Because there are many requirementsfor the scheduling, the scheduling consumes time costs and efforts. Therefore, we propose the technique to createand optimize the schedule of the short time workers in order to reduce the burden of the manager. A cooperativeevolution is applied for generating and optimizing the shift schedule of short time worker. Several problems hasbeen found in carrying out this research. This paper proposes techniques to automatically create and optimize theshift schedule of workers in large-scale HIC by using a Cooperative Evolution (CE), to solve the situation thatmany workers concentrate in a speci c time zone, and to solve the situation where many breaks are concentratedin a speci c break time zone, and an effective mutation operators.

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Study on Cooperative Evolution Behaviors in Spatial Public Goods Game Based on Self-Questioning Mechanism

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.1783 ◽

2013 ◽

Vol 380-384 ◽

pp. 1783-1787

Author(s):

Rui Xue Feng ◽

Juan Ge

Keyword(s):

Game Theory ◽

Public Goods ◽

Evolutionary Game Theory ◽

Cooperative Behavior ◽

Evolutionary Game ◽

The Self ◽

Square Lattice ◽

Public Goods Game ◽

Intensity Parameter ◽

Cooperative Evolution

We introduce a self-questioning mechanism under spatial public goods game in the framework of Evolutionary Game Theory where players are located on a square lattice and realize it by a intensity parameter a. By stimulation and analysis, we find that compared with the original Fermi updating (a=0), the introduction of the self-questioning (a>0) can be better promote cooperative behavior at the smaller r. Subsequently, we stimulate in self-questioning mechanism (a=1), the cooperator frequency fc as a function of the factor r for different values of noise K. Results show that at the larger and smaller noise K, the system presents a considerably different cooperation phenomenon. Whats more, fc as a function of r has center symmetry nature about point (5.0, 0.5) whatever the noise K is. Further analysis indicates the reasons for the formation of these phenomena. Finally, we report the agents average payoff in the steady state and its reasons for it.

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