human language, over its evolutionary history, has emerged as one of the fundamental defining characteristic of the modern man. however, this milestone evolutionary process through natural selection has not left any ’linguistic fossils’ that may enable us to trace back the actual course of development of language and its establishment in human societies. lacking analytical tools to fathom the cr...

In this paper, two games that play a role in creating a cancer tumor and suppression are studied using evolutionary game theory and its different modes are analyzed. The first game is the competition between a cancer cell and a healthy cell to receive food through the blood. In the second game, the interaction between the two oncogenes Ras and Myc is examined for cellular deformation

This paper presented a new particle swarm optimization based on evolutionary game theory (EPSO) for the traveling salesman problem (TSP) to overcome the disadvantages of premature convergence and stagnation phenomenon of traditional particle swarm optimization algorithm (PSO). In addition ,we make a mapping among the three parts discrete particle swarm optimization (DPSO)、 evolutionary game the...

The game Fox and Geese is solved using retrograde analysis. A neural network trained using a co-evolutionary genetic algorithm with the help of the expert knowledge database was found to be a very capable Fox and Geese player after training, and quickly learned to beat training opponents. Key-Words: Game theory, rote-learning, neural networks, genetic algorithms, co-evolution.

Network throughput and energy conservation are two conflicting important performance metrics for wireless sensor networks. Since these two objectives are in conflict with each other, it is difficult to achieve them simultaneously. In this paper, a joint duty cycle scheduling and energy aware routing approach is proposed based on evolutionary game theory which is called DREG. Making a trade-off ...

This paper links the theory of genetic algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a speci"c form of an evolutionary game. It will be pointed out that GA learning results in a series of near Nash equilibria which during the learning process build up to "nally approach a neighborhood of an evolutionarily stabl...