This paper presents genetic algorithm based solution for determing alignment of multiple molecular sequences. Two datasets from DNA families Canis_familiaris and galaxy dataset have been considered for experimental work & analysis. Genetic operators like cross over rate, mutation rate can be defined by the user. Experiments & observations were recorded w.r.t variable parameters like fixed popul...

In this study two combined mutation operators have been developed to increase the performance of Genetic Algorithm that helps to find the minimum cost in the known Travelling Salesman problem (TSP).In order to do this the combined mutation operators which are named as Inverted Exchange and Inverted Displacement are compared with four existing mutation operators. These are programmed in C++ and ...

Offshore engineering construction projects are large and complex, having the characteristics of multiple execution modes resource constraints. Their complex internal scheduling processes can be regarded as resourceconstrained project problems (RCPSPs). To solve RCPSP in offshore more rapidly, a hybrid genetic algorithm was established. defects algorithms, which easily fall into local optimal so...

In genetic algorithms, commonly used crossover operators such as one-point, two-point and uniform crossover operator are likely to destroy the information obtained in the evolution because of their random choices of crossover points. To overcome this defect, a new adaptive crossover operator based on the Rough Set theory is proposed in this paper. By using this specialized crossover operator, u...

(i) We investigate spectral and geometric properties of the mutation-crossover operator in a genetic algorithm with general-size alphabet. By computing spectral estimates, we show how the crossover operator enhances the averaging procedure of the mutation operator in the random generator phase of the genetic algorithm. By mapping our model to the multi-set model often investigated in the litera...

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancin...

Crossover is an important genetic operation that helps in random recombination of structured information to locate new points in the search space, in order to achieve a good solution to an optimization problem. The conventional crossover operation when applied on a pair of binary strings will usually not retain the total number of 1’s in the offsprings to be the same as that of their parents. B...

In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA's used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both. To circumvent these expensive operations, we hybridize GA with a...

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by in...