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## Deceptive

## Two dimensional view |
## One dimensional view |

## Function

## Latex

A maximization problem:

$$f(x_0 \cdots x_n) = (\sum_{i=1}^n \begin{cases}
\frac{-x_i}{d} + 1 & x_i

$$d = 0.2$$
This is the inverse of deceptiveness.

$$db = 0.7$$
This is the fitness of the best deceptive optimum.

$$0 \leq x_i \leq 1$$

$$\text{maximum at }f(0, \cdots, 0) = 1.0$$

## Python

self.deceptiveness = 0.20 #This is actually more like the inverse of deceptiveness since smaller = more deceptive. self.best_fitness = 1.0 self.deceptive_best = 0.7 def fitnessFunc(self, chromosome): fitness = 0 dimensions = len(chromosome) for i in range(dimensions): if chromosome[i] < self.deceptiveness: #Then fitness value is on a negative slope with a y #intercept at 1 fitness += chromosome[i]*(-1.0/self.deceptiveness) \ + self.best_fitness else: #Otherwise, the fitness value is on a positive slope #with an x intercept at deceptiveness fitness += (chromosome[i]-self.deceptiveness)* \ (self.deceptive_best/(1.0-self.deceptiveness)) return fitness/float(dimensions)