Parameter analysis on multi‐factorial evolutionary algorithm

Evolutionary Algorithm Parameter Tuning with Sensitivity Analysis

This article introduces a generic sensitivity analysis method to measure the influence and interdependencies of Evolutionary Algorithms parameters. The proposed work focuses on its application to a Parallel Asynchronous Cellular Genetic Algorithm (PA-CGA). Experimental results on two different instances of a scheduling problem have demonstrated that some metaheuristic parameters values have lit...

Recent Advances in Real-Parameter Evolutionary Algorithm

FPBIL: A Parameter-free Evolutionary Algorithm

The purpose of this chapter is to describe a new algorithm named FPBIL (parameter-Free PBIL), an evolution of PBIL (Population-Based Incremental Learning). FPBIL, as well as PBIL (Baluja, 1994), Genetic Algorithms (GAs) (Holland, 1992) and others are general purpose population-based evolutionary algorithms. The success of GAs is unquestionable (Goldberg, 1989). Despite that, PBIL has shown to b...

A Dimensionless Parameter Approach based on Singular Value Decomposition and Evolutionary Algorithm for Prediction of Carbamazepine Particles Size

The particle size control of drug is one of the most important factors affecting the efficiency of the nano-drug production in confined liquid impinging jets. In the present research, for this investigation the confined liquid impinging jet was used to produce nanoparticles of Carbamazepine. The effects of several parameters such as concentration, solution and anti-solvent flow rate and solvent...

Parameter Analysis on Fruit Fly Optimization Algorithm

Fruit fly algorithm is a novel intelligent optimization algorithm based on foraging behavior of the real fruit flies. In order to find optimum solution for an optimization problem, fixed parameters are obtained as a result of manual test in fruit fly algorithm. In this study, it is aimed to find the optimum solution by analyzing the constant parameter concerning the direction of the algorithm i...