In this paper we study random genetic drift in a nite genetic population Exact formulae for calculating the mean convergence time of the population are analytically derived and some results of numerical calculations are given The calculations are compared to the results obtained in population genetics A new proposition is derived for binary alleles and uniform crossover Here the mean convergenc...

In traditional Genetic Algorithm, random population seeding technique is simple and efficient however the population may contain poor quality individuals which take long time to converge optimal solution. This motivates to design a population initialization technique with the features of randomness, individual diversity and good quality. In this paper, an initial work has been carried out to de...

This paper proposes a new self-adaptive genetic algorithm。This new algorithm divides the whole evolution process into three stages. At each stage, the new algorithm adopts different operation method. The main ideas are grading balance selection, continuous crossover operation. The new algorithm designs especially self-adaptive mutation probability according to the principle of searching for thi...

The influence of different parallel genetic algorithm (PGA) architectures on the GA convergence properties is analysed. Next, two proposed versions of these PGA architectures are compared – homogenous and heterogeneous. Finally the effect of re-initialisation in some partial populations on the PGA convergence has been analysed. The proposed PGA modifications are useful mainly in case of non-smo...

A parallel two-level evolutionary algorithm which evolves genetic algorithms of maximum convergence velocity is presented. The meta-algorithm combines principles of evolution strategies and genetic algorithms in order to optimize continuous and discrete parameters of the genetic algorithms at the same time (mixed-integer optimization). The genetic algorithms which result from the meta-evolution...

The present article puts forward a method for an interactive model generation through the use of Genetic Algorithms applied to small populations. Micropopulations actually worsen the problem of the premature convergence of the algorithm, since genetic diversity is very limited. In addition, some key factors, which modify the changing likelihood of alleles, cause the likelihood of premature conv...

There are two primary objectives of this dissertation. The first goal is to identify certain limits of genetic algorithms that use only fitness for learning genetic linkage. Both an explanatory theory and experimental results to support the theory are provided. The other goal is to propose a better design of the linkage learning genetic algorithm. After understanding the cause of the performanc...

Having the constant population size and crossover/mutation probability, standard genetic algorithm (SGA) has such disadvantages as premature convergence, low stability and optimization efficiency for large design variables situation. This paper presents an improved adaptive genetic algorithm (IAGA), which adjusts the population size and the crossover/mutation probability adaptively and linearly...

Several metrics are used in empirical studies to explore the mechanisms of convergence of genetic algorithms. The metric is designed to measure the consistency of an arbitrary ranking of hyperplanes in a partition with respect to a target string. Walsh coeecients can be calculated for small functions in order to characterize sources of linear and nonlinear interactions. A simple deception measu...