Genetic operator

The intact animal can be improved for experimental purposes if it is rendered abnormal in some way, by genetic malfunction, by illness, or by operation. Genetic defects, or mutations, are used widely in the study of bacterial metabolism, where they can be read ily induced, for example through irradiation by X-rays or from a radioactive source. Genetic defects frequently reveal themselves in the form of the absence of one specific enzyme, and metabolic studies with such enzymically defective preparations are of the same type as those made possible by the use of a specific enzymic inhibitor which we discussed above. Genetic defects in animals are rarer, but classic cases of the absence of specific enzymes and hence the accumulation of abnormal metabolites are provided in humans by the genetically carried diseases of phenylketonuria and alkaptonuria. In both, unusual substances are excreted in the urine, and the analysis of the reasons for their appearance has led to valuable information about the mechanism of amino acid metabolism in the body. 

A prerequisite for a well-operating genetic algorithm is a wellperforming random generator. However, one should note that genetic algorithms are not solely based on chance (randomness), as is valid for Monte Carlo simulations. In this case, randomness is only a tool to successfully proceed in the space of the objective area. 

The evolutionary process of a genetic algorithm is accomplished by genetic operators which translate the evolutionary concepts of selection, recombination or crossover, and mutation into data processing to solve an optimization problem dynamically. Possible solutions to the problem are coded as so-called artificial chromosomes, which are changed and adapted throughout the optimization process until an optimrun solution is obtained. 

Selection alone cannot achieve an optimization towards the solution With mere scicction performed over a number of generations, one would get a population which comprises only the best chromosome of the original population. Therefore, an operator has to be applied which causes variance within the population, This is achieved by the application of genetic operators such as the crossover and the mutation operators. 

Crossover, which is also called recombijiation, follows the idea that aji offspring in natiu c always holds genes from both its parents. Accordingly, the genetic crossover operator takes parts of two parent chromosomes to create a new offspring. 

Whatever the physiology of odor perception may be, the sense of smell is keener than that of taste (22). If flavors are classed into odors and tastes as is common practice in science, it can be calculated that there are probably more than 10 possible sensations of odor and only a few, perhaps five, sensations of taste (13,21,35—37). Just as a hereditary or genetic factor may cause taste variations between individuals toward phenylthiourea, a similar factor may be in operation with odor. The odor of the steroid androsterone, found in many foods and human sweat, may eflcit different responses from different individuals. Some are very sensitive to it and find it unpleasant. To others, who are less sensitive to it, it has a musk or sandalwood-like smell. Approximately 50% of the adults tested cannot detect any odor even at extremely high concentrations. It is befleved that this abiUty is genetically determined (38). 

During the early 1970s, the necessary telecommunications technology became available with packet switching. ARPANet, the first operational packet-switched digital communications network, was implemented by the U.S. Department of Defense. Commercial systems (eg. Telenet, TYMNET, and GENet) became available shortiy thereafter. 

Figure 2 Genetic operators used to create a population of children chromosomes from a population of parent chromosomes, (a) Single-point mutation. A gene to he mutated is selected at random, and its value is modified, (b) One-point crossover. The crossover point is selected randomly, and the genes are exchanged between the two parents. Two children are created, each having genes from both parents.

Repressor and Cro proteins operate a procaryotic genetic switch region... 

These genetic experiments clearly demonstrated that the proposed structural model for the binding of these proteins to the phage operators was essentially correct. The second a helix in the helix-turn-helix motif is involved in recognizing operator sites as well as in the differential selection of operators by P22 Cro and repressor proteins. However, a note of caution is needed many other early models of DNA-protein interactions proved to be misleading, if not wrong. Modeling techniques are more sophisticated today but are still not infallible and are certainly not replacements for experimental determinations of structure. 

The elegant genetic studies by the group of Charles Yanofsky at Stanford University, conducted before the crystal structure was known, confirm this mechanism. The side chain of Ala 77, which is in the loop region of the helix-turn-helix motif, faces the cavity where tryptophan binds. When this side chain is replaced by the bulkier side chain of Val, the mutant repressor does not require tryptophan to be able to bind specifically to the operator DNA. The presence of a bulkier valine side chain at position 77 maintains the heads in an active conformation even in the absence of bound tryptophan. The crystal structure of this mutant repressor, in the absence of tryptophan, is basically the same as that of the wild-type repressor with tryptophan. This is an excellent example of how ligand-induced conformational changes can be mimicked by amino acid substitutions in the protein. 

Goldberg, D.E. (1983) Computer-aided gas pipeline operation using genetic algorithms and rule learning (Doctoral dissertation. University of Michigan). Dissertation Abstracts International, 44(10), p. 3174B (University Microfilms No. 8402282). 

Figure 11.11 shows examples of the three basic genetic operations of reproduction, crossover and mutation, as applied to a population of 8-bit chromosomes. Reproduction makes a set of identical copies of a given chromosome, where the number of copies depends on the chromosome s fitness (see below). The crossover operator exchanges subparts of two chromosomes, where the position of the crossover is randomly selected, and is thus a crude facsimile of biological sexual recombination between two single-chromosome organisms. The mutation operator randomly flips one or more bits in the chromosome, where the bit positions are randomly chosen. 

Fig. 11.11 Schematic representation of the basic genetic operations of reproduction, crossover and mutation.

Step 4 Randomly pairing up the chromosomes in the new population, apply the genetic crossover operator to each pair. That is, randomly select a bit-position, say k, for each pair of chromosomes, say and and replace this pair with two new pairs - and - constructed via genetic < rossover C, consists of the first k bits of C,i, and the last N — k) bits of and C, consists of the first k bits of and the last N — k) bits of. ... 

Table 11.3 One pass (read left to right) through the step.s of a basic genetic algorithm scheme to maximize the fitness function f x) = using a population of six 6-bit chromosomes. The crossover notation aina2) means that chromosomes Ca, and Ca2 exchange bits beyond the bit. The underlined bits in the Mutation Operation column are the only ones that have undergone random mutation. See text for other details.

Next, we randomly pair up the new chromosomes, and perform the genetic crossover operation at randomly selected bit-positions -- chromosomes C and C4 exchange their last three bits, C2 and Cg exchange their last four bits, and C3 and C5 exchange their last bit ... 

Process A Genetic instability Substrate repression Multi-step synthesis Product (volatile) inhibition Mode of operation Batch Fed-batch Continuous... 

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