This paper describes a new approach for parameter optimization that uses a novel representation for the parameters to be optimized. By using genetic programming, the new method evolves functions that transform initial random values for the parameters into optimal ones. This new representation allows the incorporation of knowledge about the problem being solved to improve the search. Moreover, t...

The further study on the sensitivity analysis of Neocognitron is discussed in this paper. Fukushima's Neocognitron is capable of recognizing distorted patterns as well as tolerating positional shift. Supervised learning of the Neocognitron is fulfilled by training patterns layer by layer. However, many parameters, such as selectivity and receptive fields are set manually. Furthermore, in Fukush...

In this paper, a technique based on genetic algorithms is proposed for improving the accuracy of solar cell parameters extracted using conventional techniques. The approach is based on formulating the parameter extraction as a search and optimization problem. Current–voltage data used were generated by simulating a two-diode solar cell model of specified parameters. The genetic algorithm search...

This paper presents an adaptive algorithm that can adjust parameters of genetic algorithm according to the observed performance. The parameter adaptation occurs in parallel to the running of genetic algorithm. The proposed method is compared with the algorithms that use random parameter sets and a standard parameter set. It is shown to be the most promising method from two performance measureme...

The parameter-less genetic algorithm was introduced a couple of years ago as a way to simplify genetic algorithm operation by incorporating knowledge of parameter selection and population sizing theory in the genetic algorithm itself. This paper shows how that technique can be used in practice by applying it to a network expansion problem. The existence of the parameter-less genetic algorithm s...

Genetic Algorithms (GA) is a family of search algorithms based on the mechanics of natural selection and biological evolution. They are able to efficiently exploit historical information in the evolution process to look for optimal solutions or approximate them for a given problem, achieving excellent performance in optimization problems that involve a large set of dependent variables. Despite ...

In biosorption research, a fairly broad range of mathematical models are used to correlate discrete data points obtained from batch equilibrium, batch kinetic or fixed bed breakthrough experiments. Most of these models are inherently nonlinear in their parameters. Some of the models have enjoyed widespread use, largely because they can be linearized to allow the estimation of parameters by leas...

The Levenberg-Marquardt (LM) minimization algorithm commonly employed in MOSFET model parameter extraction has several known deficiencies, such as poor convergence characteristics without a good initial guess, low likelihood of convergence to the globally optimal solution, and difficulty with simultaneous multiobjective optimizations. Furthermore, conventional tools require an expert user with ...