In theory, there is no difference between theory and practice. But, in practice, there is. To leverage theoretical and practical works in the field of genetic programming (GP), the Genetic Programming Theory and Practice (GPTP) Workshop series was conceived and launched in 2003. For the past two years, theoreti-cians and practitioners have come to Ann Arbor to present their works and to listen ...

This paper deenes a class of evolutionary algorithms called evolutionary pattern search algorithms (EPSAs) and analyzes their convergence properties. This class of algorithms is closely related to evolutionary programming, evolution strategie and real-coded genetic algorithms. EPSAs are self-adapting evolutionary algorithms that modify the step size of the mutation operator in response to the s...

In the traditional immune genetic algorithm, crossover and mutation can disrupt the superior chromosome, so make the algorithm took a long time to converge to the best solution. The way of crossover and mutation based on marginal product model which can make the algorithm converge quickly was proposed in order to avoid the disruption of the superior chromosome. The pseudo parallel evolution mec...

A route planning method based on gradient-field quantum genetic algorithm model was presented in this paper. It introduces the gradient field of a grid map to quantum genetic algorithm model and uses quantum genetic algorithm (QGA) to optimize the cost function of route planning. By combining the quantum characteristics with the capabilities of the large diversity of the population, as well as ...

In this paper, we describe a new mechanism of cellular selection as an improved Genetic Algorithm for some optimization problems like Cellular Channel assignment, which have multi feasible/optimum solution per one case. Considering the problems and the nature of relationship among individuals in population, we use 2-dimension Cellular Automata in order to place the individuals onto its cells to...

Premature convergence is one of the important issues while using Genetic Programming for data modeling. It can be avoided by improving population diversity. Intelligent genetic operators can help to improve the population diversity. Crossover is an important operator in Genetic Programming. So, we have analyzed number of intelligent crossover operators and proposed an algorithm with the modific...