The genetic algorithm

Mutation is allowed to oeeur in some of the offsprings, the amount being eon-trolled by the mutation rate, typieally a very small number. This results in the random ehange in a gene in an offspring, i.e. from 0 to 1. 

The breeding of sueeessive generations eontinues until all offsprings are aeeeptably fit. In some eases, all offsprings will eventually have the same genetie strueture. 

Determine the offsprings from the initial generation and the subsequent generation. Solution 

Values between 0 and 0.342, Parent 1 seleeted Values between 0.343 and 0.488, Parent 2 seleeted 

Parent 2 10 J =f(x) P — J jUJ Cumulative probability Roulette wheel bits 

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This completes one complete cycle of the genetic algorithm. The new papulation then becomes the current population ready for a new cycle. The algorithm repeatedly applies this sequence for a predetermined number of iterations and/or until it com erges. 

T Dandekar, P Argos. Folding the mam chain of small proteins with the genetic algorithm. J Mol Biol 236 844-861, 1994. 

To determine the optimal parameters, traditional methods, such as conjugate gradient and simplex are often not adequate, because they tend to get trapped in local minima. To overcome this difficulty, higher-order methods, such as the genetic algorithm (GA) can be employed [31,32]. The GA is a general purpose functional minimization procedure that requires as input an evaluation, or test function to express how well a particular laser pulse achieves the target. Tests have shown that several thousand evaluations of the test function may be required to determine the parameters of the optimal fields [17]. This presents no difficulty in the simple, pure-state model discussed above. 

The main steps of the ABS topology design with the genetic algorithm are as follows ... 

Unger R (2004) The Genetic Algorithm Approach to Protein Structure Prediction 110 153-175... 

Spalek, T., Pietrzyk, P. and Sojka, Z. (2005) Application of the genetic algorithm joint with the Powell method to nonlinear least-squares fitting of powder EPR spectra,. /. Chem. Inf. Model., 45, 18. 

The genetic algorithm is unimpressive, at least to begin with (line 1). The quality of the fit that it finds does improve (lines 2 and 3) and eventually it will reach a solution that matches the least squares fit, but the algorithm takes far longer to find the solution than standard methods do, and the fit is no better. 

The prediction of stable structures that can be formed by groups of a few dozen atoms is computationally expensive because of the time required to determine the energy of each structure quantum mechanically, but such studies are increasingly valuable because of the need in nanochemistry to understand the properties of these small structures. The genetic algorithm is now widely used to help predict the stability of small atomic clusters.2... 

Before we can start to use the genetic algorithm, we must answer the question What exactly is a "population of solutions " It is easy to envisage a population of crocodiles or ants or lamas, but what does a population of solutions look like ... 

The vector that the genetic algorithm manipulates is known conventionally as a chromosome or a string we shall use the latter terminology in this chapter. The individual units from which each string is constructed, a single x-, y-, or z-coordinate for an atom in this example, are referred to as genes. 

While floating-point values are used to construct the strings in most scientific applications, in some types of problem the format of the strings is more opaque. In the early development of the genetic algorithm, strings were formed almost exclusively out of binary digits, which for most types of problem are more difficult to interpret letters, symbols, or even virtual objects... 

Example 2 Energy Minimization Using the Genetic Algorithm... 

The surface defined by an arbitrary fitness function, across which the genetic algorithm (GA) searches for a maximum or minimum. The surface may be complex and contain numerous minima and maxima. 

The Genetic Algorithm Parameters Used in the Dipoles Problem... 

How Does the Genetic Algorithm Find Good Solutions ... 

The term "fitness" is used because, as you might have guessed, the genetic algorithm is the favorite tool for this manipulation. 

There are three steps in the evolution of the classifier list (1) the identification of useful classifiers, (2) the creation of new classifiers that may be of value, and (3) the removal of classifiers that serve no useful purpose. This is just the sort of task for which the genetic algorithm is designed. It assesses individuals on the basis of their quality, selects the better individuals, and from them creates new, potentially better, individuals. 

After the chosen number of cycles has passed, the genetic algorithm is applied to the set of classifiers. The fitness of each classifier may be related directly to its strength, or the fitness may be determined by combining classifier strength with other factors, such as the specificity. The usual GA operators are applied to create a new population of classifiers, which is then given the opportunity to control the environment for many cycles. The process continues until overall control is judged to be adequate under all circumstances. 

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