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The Fitness Landscape

Landscapes with a single smoothly increasing bump, such as the one shown in figure 11.12-a, for example, are usually amenable to any systematic climb towards larger values. On the other hand, landscapes with a. single isolated maximum that sits on an otherwise eveii-kweled surface may not be so easy to solve, because at no point on the surface is there a clue as to which direction to proceed in to move towards the maximum. [Pg.586]


Boosting is crucial for the successful operation of the pattern-recognition GA because it modifies the fitness landscape by adjusting the values of the class and sample weights. This helps to minimize the problem of convergence... [Pg.418]

The fitness function is simply the mapping between points in sequence space and their fitnesses. The fitness landscape is the combination of the fitness function and the neighbor relationship. With neighbors defined by point mutation, for an N-site molecule, the landscape is the N-dimensional surface that results from plotting the fitness function over an N-dimensional Cartesian coordinate sequence space (Fig. 13). [Pg.126]

Fig. 6. Effects of stringent selection on the fitness landscape. The apparent number of functional peaks is a function of stringency. Fig. 6. Effects of stringent selection on the fitness landscape. The apparent number of functional peaks is a function of stringency.
Fig. 7. Representations of clusters of sequences, (a) A mesa in the fitness landscape would represent a cluster of sequences with similar activities, (b) A peak would represent a cluster of sequences that embody a gradient... Fig. 7. Representations of clusters of sequences, (a) A mesa in the fitness landscape would represent a cluster of sequences with similar activities, (b) A peak would represent a cluster of sequences that embody a gradient...
The fitness landscape also affects the reproducibility of the experiment. If the landscape is very smooth, then there are many possible paths between two sequences (Wang et al., 1996). However, as the ruggedness of the landscape increases, the number of paths sharply decreases. [Pg.101]

The benefit of allowing mutants that are less fit than the parent is dependent on the ruggedness of the fitness landscape. If the landscape is very rugged, then an initially bad mutation may be required to ultimately benefit from a coupling interaction. In nature, many mechanisms allow unfit genes to survive. For example, some genes are recessive or are... [Pg.103]

The quasispecies model defines an optimal mutation rate for evolving populations (Eigen et al., 1988). At the critical mutation rate pmml (referred to as the error threshold), the distribution becomes too broad for selection to withstand the dispersion and it wanders stochastically on the fitness landscape. The optimal mutation rate for evolvability should be as close to pm Crit as possible without exceeding it. Indeed, it was found that viral mutation rates are very close to pm m,. By assuming that the mutation probability is the same at each residue, the error threshold in terms of mutation rate pm ai, was derived as... [Pg.104]

Fig. 7. The probability distribution P(c) of a positive mutation with c coupled interactions occurs as the sequence ascends the fitness landscape (generated using a spin-glasslike fitness function). The distribution is shown at two positions on the fitness landcape, a random sequence (O) and a highly optimized sequence (A). As the sequence is optimized, the probability that positive mutations will be made at uncoupled residues increases considerably. The mutation rate is an average of one amino acid (three nucleotides) per sequence and the number of mutants screened is 3000. Reprinted from Voigt et at. (2000b), with permission. Fig. 7. The probability distribution P(c) of a positive mutation with c coupled interactions occurs as the sequence ascends the fitness landscape (generated using a spin-glasslike fitness function). The distribution is shown at two positions on the fitness landcape, a random sequence (O) and a highly optimized sequence (A). As the sequence is optimized, the probability that positive mutations will be made at uncoupled residues increases considerably. The mutation rate is an average of one amino acid (three nucleotides) per sequence and the number of mutants screened is 3000. Reprinted from Voigt et at. (2000b), with permission.
To determine the optimal number of cut points, it is necessary to return to the balance of exploration and exploitation. Uniform crossover is more exploratory than single-point crossover because the number of schema that can be sampled increases (Spears, 1993). However, the exploration comes at the cost of exploitation because more of the schema are disrupted (Eschelman et al., 1989). The specific balance between these effects will depend on the sampling ability of the library, the structure of the schema, and the ruggedness of the fitness landscape. [Pg.119]

Macken et al. (1991) identified two behaviors of an adaptive walk as it ascends the fitness landscape that are separated by a distinct phase transition. As a sequence becomes more optimized, the number of fitter mutants decreases. At some point, it becomes difficult to sample a fitter mutant given the number that can be screened. The probability of a sequence with fitness I having a single-mutant neighbor with lower fitness is G(F). The probability that a sequence with D single-mutant neighbors is at a local optimum 1 (F) is approximately... [Pg.125]


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