Phylogenetic reconstructions are a major component of many studies in evolutionary biology, but their accuracy can be reduced under certain conditions. Recent studies showed that the convergent evolution of some phenotypes resulted from recurrent amino acid substitutions in genes belonging to distant lineages. It has been suggested that these convergent substitutions could bias phylogenetic rec...

This paper extends the theoretical analysis of the Adaptive Reservoir Genetic Algorithm (ARGA), a variant of a Genetic Algorithm (GA) proposed by the authors in [4]. We show that ARGA visits the global optimum after a finite number of iterations with probability one, regardless of the initialization of the population. Key-Words: Genetic Algorithms, convergence analysis, adaptive control of search.

An essential defect in tumorigenesis is the imbalance between the tendency of neoplastic cells to divide and spread throughout the body, and the capacity of the organism to regulate and restrain such growth. In principle, two main explanatory strat egies have in recent medical history been followed when at tempting to understand the events of carcinogenesis. In the holistic or integrationist ap...

It is an important and a complicated task to investigate the convergence of a new genetic algorithm based on quantum mechanics concepts including qubits and superposition of states, namely Quantum Genetic Algorithm, in the field of evolutionary computation. This paper analyzes convergence property of quantum genetic algorithm which uses its special quantum operator instead of canonical operator...

The definition of the hardness of a problem for GA’s has been tackled, eventually leading to the notion of deception [Gol89, HG94, Dav87]. It has been known for a while that the hardness of a problem is inherently related to the representation that is used. This fact will be illustrated below by showing that an easy problem (1’s counting problem) can become nearly unsolvable after a change of r...

We discuss the use of normal distribution theory as a tool to model the convergence characteristics of di erent GA selection schemes The models predict the proportion of optimal alleles in function of the number of generations when optimizing the bit counting function The selection schemes analyzed are proportionate selection tournament selec tion truncation selection and elitist recombination ...

We use the average hamming distance of a population as a syntactic metric to obtain probabilistic bounds on the time convergence of genetic algorithms. Analysis of a at function provides worst case time complexity for static functions and gives a theoretical basis to the problem of premature convergence. We suggest simple changes that mitigate this problem and help ght deception. Further, emplo...

the aim of this study was using bayesian approach via gibbs sampling (gs) for estimating genetic parameters of production, reproduction and health traits in iranian holstein cows. data consisted of 320666 first- lactation records of holstein cows from 7696 sires and 260302 dams collected by the animal breeding center of iran from year 1991 to 2010. (co) variance components were estimated using ...

This paper introduces and analyzes a parallel method of simulated annealing. Borrowing from genetic algorithms, an eeective combination of simulated annealing and genetic algorithms, called parallel recombinative simulated annealing, is developed. This new algorithm strives to retain the desirable asymptotic convergence properties of simulated annealing, while adding the populations approach an...

Multilayer perceptrons (MLPs) are widely used for pattern classification and regression problems. Backpropagation (BP) algorithm is known technique in the training of multilayer perceptrons. However for its optimum training convergence, the learning and momentum parameters need to be tuned on trial and error method. Further, sometimes the backpropagation algorithm fails to achieve global conver...