In this work we consider the extension of Genetic-Independent Component Analysis Algorithms (GA-ICA) with guiding operators and prove their convergence to the optimum. This novel method for Blindly Separating unobservable independent component Sources (BSS) consists of novel guiding genetic operators (GGA) and finds the separation matrix which minimizes a contrast function. The convergence is s...

This paper is a survey of some results obtained in evolutionary genetics by algebraical methods. The main topics: 1 . Some special classes bf algebras which arising in population genetics (stochastic. haric, RmtxsTEINian, genetic etc.). 2. The BERNSTEIS’S problem and the stationary genetic structure. 3. The convergence to an equilibrium. the rate of convergence. 4. The exact linearization of ev...

In view of the slowness and the locality of convergence for Simple Genetic Algorithm (SGA) in solving complex optimization problems, we propose an improved genetic algorithm named Multi-Stage Composite Genetic Algorithm (MSC-GA) through reducing the optimization-search range gradually, and the structure and implementation steps ofMSC-GA is also discussed. Then, we consider its global convergenc...

Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain convergence theorems give general upper bounds on the linear program sizes needed for convergence. Tight bounds (exponential in N , N logN and smaller) are given for five computer models (any, average, cyclic, bit flip and Boolean). Mutation randomizes a genetic algorithm population in 1 4 (l + 1)(lo...

This paper presents a lower-bound result on the computational power of a genetic algorithm in the context of combinatorial optimization. We describe a new genetic algorithm, the merged genetic algorithm, and prove that for the class of monotonic functions, the algorithm nds the optimal solution, and does so with an exponential convergence rate. The analysis pertains to the ideal behavior of the...

For the problem of cloud service selection, this paper gives a hybrid algorithm. The hybrid algorithm is composed mainly by Simplex Method and Genetic Algorithm. In this algorithm, a tree traversal sequence encoding scheme and some Simplex Method operations are proposed. The design of the tree traversal sequence encoding can support various types of service combinations. In addition, the hybrid...

The Bacterial Evolutionary Algorithm (BEA) is a relatively new type of evolutionary algorithm and shows the typical phenomena of stochastic optimization methods. Two of these phenomena: premature convergence and low convergence speed near the optimum are often in connection with the low genetic diversity of the population. Variation of genetic diversity in the original BEA and in its three para...

ABSTRAC1 Convergence of densities leads directly to convergence of their distribution functions via Scheffe's theorem. This paper is concerned with the converse: what are sufficient conditions to obtain convergence of densities from convergence of distribution functions? A general lemma is given and results are obtained for translation and scale statistics with applications to bootstrap estimat...

In this paper we present parallel implementation of genetic algorithm using map/reduce programming paradigm. Hadoop implementation of map/reduce library is used for this purpose. We compare our implementation with implementation presented in [1]. These two implementations are compared in solving One Max (Bit counting) problem. The comparison criteria between implementations are fitness converge...

Error-correcting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting graph isomorphism. By applying genetic algorithms, some local search strategies are amalgamated to improve convergence speed. Besides, a selecti...