Genetic algorithms example

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. 

Example 2 Energy Minimization Using the Genetic Algorithm... 

Genetic Algorithms (GA) are used in case of large combinatorial problems and can be applied e g. for example in complex value chain network design decisions (Chan/Chung 2004)... 

The most distinctive feature of the algorithm is its use of a population of potential solutions, so it is reasonable to ask why it might be more effective to work with many potential solutions when conventional methods require only one. To answer this question, and to appreciate how the genetic algorithm works, we consider a simple example. 

Although it is easy to think of a solution to this problem without the need to introduce computers, it is nevertheless instructive to observe how the genetic algorithm works its way toward this solution. Potential solutions, constructed as vectors, can easily be prepared by specifying the angle that each dipole makes with the vertical axis. A typical string would then be written as an ordered list of these angles, for example ... 

The fourth example, with its six ways, leans even more toward provocadon while still being reasonably informative. The actual presentadon for which this served as a tide is about the use of six elements of practical GA theory to make genetic algorithms work better in applicadons, and this dde does hint at that, creating interest by shrouding the six ways in mystery. (If you re interested in drawing an audience to a presentation of theory, some mystery in one s title is essential.)... 

The yield of the expected reaction product was used in an example as the feedback to a genetic algorithm (GA) driven method that proposes a new set of reaction conditions. After some cycles of synthesis and analysis the yield of this reaction was significantly improved by using better reaction conditions. In a second step, a set of different MCRs using a set of different conditions for each of them was carried out in parallel and optimized with a GA to find common optimal conditions [29]. 

A separate class of experimental evaluation methods uses biological mechanisms. An artificial neural net (ANN) copies the process in the brain, especially its layered structure and its network of synapses. On a very basic level such a network can learn rules, for example, the relations between activity and component ratio or process parameters. An evolutionary strategy has been proposed by Miro-datos et al. [97] (see also Chapter 10 for related work). They combined a genetic algorithm with a knowledge-based system and added descriptors such as the catalyst pore size, the atomic or crystal ionic radius and electronegativity. This strategy enabled a reduction of the number of materials necessary for a study. 

Fig. 6.1 Operations used in genetic algorithms (values in the examples are proportions of elements in the active component of the catalyst expressed in mol%).

---