Evolutionary game theory is a powerful framework for studying evolution in populations of interacting individuals. A common assumption in evolutionary game theory is that interactions are symmetric, which means that the players are distinguished by only their strategies. In nature, however, the microscopic interactions between players are nearly always asymmetric due to environmental effects, d...

evolutionary algorithms are some of the most crucial random approaches tosolve the problems, but sometimes generate low quality solutions. on the otherhand, learning automata are adaptive decision-making devices, operating onunknown random environments, so it seems that if evolutionary and learningautomaton based algorithms are operated simultaneously, the quality of results willincrease sharpl...

Different mutation operators have been proposed in evolutionary programming, but for each operator there are some types of optimization problems that cannot be solved efficiently. A mixed strategy, integrating several mutation operators into a single algorithm, can overcome this problem. Inspired by evolutionary game theory, this paper presents a mixed strategy evolutionary programming algorith...

Due to the resource constraint and dynamic parameters, reducing energy consumption became the most important issues of wireless sensor networks topology design. All proposed hierarchy methods cluster a WSN in different cluster layers in one step of evolutionary algorithm usage with complicated parameters which may lead to reducing efficiency and performance. In fact, in WSNs topology, increasin...

This paper discusses active learning games as a potentially important pedagogical technique in support of formal classroom education. A brief review of the active learning literature is given, followed by a list of known active learning games relevant to the CDIO engineering education context. As a specific example of an active learning game we present the learning objectives, rules, and implem...

Wang’s et al. article [1] is the first to integrate game theory (especially evolutionary game theory) with epigenetic modification of zygotic genomes. They described and assessed a modeling framework based on evolutionary game theory to quantify, how sperms and oocytes interact through epigenetic processes, to determine embryo development. They also studied the internal mechanisms for normal em...

This technical report describes first experiences in applying evolutionary game theory to a case of smart grid coordination. The use of evolutionary game theory to model population dynamics has been e↵ectively used in domains such as biology and economics but applications to cases of smart grid coordination have not been widespread. In this work, we explicitly model a population consisting of c...

Checkers is a very simple game and easy to learn. Unlike chess, it is simple to move and needs a few rules. With respect to checkers, the evolutionary algorithm can discover a neural network that can be used to play at a near-expert level without injecting expert knowledge about how to play the game. Evolutionary approach does not need any prior knowledge to develop machine player but can devel...

This paper suggests that the analysis of Schumpeterian competitionwithin the Nelson--Winter model should be complemented with evolutionary game theory. This model and its limitations for density-dependent Schumpeterian strategies are presented in terms of the equations of evolutionary dynamics. Formulated as evolutionary games, the set of strategies can easily be extended from innovators and im...

game theory is an approach based on mathematics to study social interactions and modeling strategic situations. this theory focuses on decision situations where preferences of decision makers are disputed. existence of strategic position in city also with a conflict of interest or competition makes using game theory to describe the urban management issues, attractive. one of the important contr...