Finding global optima in the continuous domain is challenging for Genetic Algorithms (GAs). Traditional GAs use either binary-coded or real-coded representation of the variables, but there is a trade-off between these two encoding methods. Recombination operators for binary-coded GAs are simple to design, but the length of the string representing an individual would be huge if the number of des...

this paper considers the job scheduling problem in virtual manufacturing cells (vmcs) with the goal of minimizing two objectives namely, makespan and total travelling distance. to solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (nsga-ii) and knowledge-based non-dominated sorting genetic algorithm (kbnsga-ii). the difference between these algor...

-Recombination operator plays a very important role in genetic algorithms. In this paper, we present binary coded genetic algorithms in which more than two parents are involved in recombination operation. We propose two types of multi-parent recombination operators, the multi-cut (MX) and seed crossover (SX). Each of these operators is a natural generalization of two parents recombination opera...

We propose repair pipelining , a technique that speeds up the performance in general erasure-coded storage. By carefully scheduling of failed data small-size units across storage nodes pipelined manner, reduces single-block time to approximately same as normal read for single block homogeneous environments. further design different extensions algorithms heterogeneous environments and multi-bloc...

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...

Classification problem is one of the wellstudied problems in machine learning. By supervised way we can solve it. First step is the generation of IF-THEN rules at the learning phase from records where class is known. Second step is to use those rules to classify records where class is not known. In this article we are using real coded multi objective genetic algorithms (MOGAs) for generating a ...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...