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Maze[rowIndex].

Don’t forget that statistics rule the performance of genetic algorithms, so you can’t evaluate the performance of an algorithm or a setting with only one run – you’ll want to run at least 10 trials of each different setting before judging its performance. Summary In this chapter, you’ve learned the basics of implementing a genetic algorithm. The pseudocode at the beginning of the chapter provides a generic conceptual model for all genetic algorithms you’ll implement throughout the rest of the book: each genetic algorithm will initialize and evaluate a population, and then enter a loop that performs crossover, mutation, and re-evaluation.

If an individual is selected for crossover then a second parent needs be found. To find the second parent, we need to pick one of many possible selection methods. Roulette Wheel Selection Roulette wheel selection - also known as fitness proportionate selection - is a selection method which uses the analogy of a roulette wheel to select individuals from a population. The idea is that individuals from the population are placed on a metaphoric roulette wheel depending on their fitness value. The higher the fitness of the individual, the more space it’s allocated on the roulette wheel.

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Algorithms for Comm. Systs and Their Applns by N. Benvenuto, et al.,


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