Hybridizing Adaptive Genetic Algorithm with Chaos Searching Technique for Numerical Optimization

Hybridizing Adaptive Genetic Algorithm with Chaos Searching Technique for Numerical Optimization

Genetic algorithm (GA) is a population-based approach for heuristic search in optimization problems based on the principle of biologic evolution and natural selection. In this paper, we present a hybrid adaptive genetic algorithm with chaos searching technique for numerical optimization. On the one hand, two sets of crossover and mutation rates are formulated to automatically maintain the balan...

Airfoil Shape Optimization with Adaptive Mutation Genetic Algorithm

An efficient method for scattering Genetic Algorithm (GA) individuals in the design space is proposed to accelerate airfoil shape optimization. The method used here is based on the variation of the mutation rate for each gene of the chromosomes by taking feedback from the current population. An adaptive method for airfoil shape parameterization is also applied and its impact on the optimum desi...

An optimization technique for vendor selection with quantity discounts using Genetic Algorithm

Vendor selection decisions are complicated by the fact that various conflicting multi-objective factors must be considered in the decision making process. The problem of vendor selection becomes still more compli-cated with the inclusion of incremental discount pricing schedule. Such hard combinatorial problems when solved using meta heuristics produce near optimal solutions. This paper propose...

Efficient Numerical Optimization Technique Based on Real-Coded Genetic Algorithm

An Adaptive Evolutionary Algorithm for Numerical Optimization

In this paper, a normalized oating point representation has been used for making it be possible to design biotechnical genetic operators as well as to apply some genetic operators like inversion. To improve the adaptation of evolutionary algorithms and avoid the biases which may exist in some genetic operators, we have designed and applied several kinds of genetic operators with some probabilit...