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EE459 I ntroduction to Artificial I ntelligence

EE459 I ntroduction to Artificial I ntelligence. Genetic Algorithms Practical Issues: Selection. ‘Roulette’ Problems.

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EE459 I ntroduction to Artificial I ntelligence

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  1. EE459Introduction to Artificial Intelligence Genetic Algorithms Practical Issues: Selection

  2. ‘Roulette’ Problems • The basic ‘roulette’ selection, in which each individual is assigned a probability of selection according to their fitness in proportion to total fitness, suffers from a number of problems • suppose the fitness varies from e.g. -10 to +10 • we could add 10 (or 11), but suppose we don’t know the lower bound of fitness • as the solution is nearly reached, all fitnesses will be roughly the same, so have roughly equal chance, e.g. • four individuals with x= 29, 29, 30, 30 when maximising x2 over [0 31] have fitness proportions 0.242, 0.242, 0.258, 0.258

  3. Fitness Scaling • A frequently used solution is to ‘scale’ the fitness function in some way • Fitness scaling alters (transforms) the fitness in some way, such that • the fitness of an average individual is still average • below average individuals have low fitness • above average individuals have high fitness • There are two main methods used • linear scaling • sigma scaling

  4. Linear Scaling • For the population, find • the average (mean) fitness • the minimum (min) fitness • Scale by the equation 1.0 0.1 fmin fmean fmax

  5. Sigma Scaling • For the population, find • the average (mean) fitness • the standard deviation (std) of fitness • Scale by the equation • Has a very similar effect to linear scaling, except when there are outliers

  6. Elitism • If selection of the next generation is left to chance, then there is always a possibility that the best individual in the population does not survive • the GA may ‘wander off’ from a good solution • In ‘elitist’ strategies, the best individual always survives to the next generation • there are minor variations possible • the best individual survives to the next generationand is then is the pool for crossover, mutation, etc. • the best individual survives to the next generationand is ‘protected’ from any genetic operators

  7. Overlapping Populations • As a further refinement, it is also possible to not replace the entire population at each generation • straight overlapping • a proportion of the population • e.g. 50% survives unchanged, the other 50% undergo operators • elitist overlapping • temporarily allow the population size to grow by adding in new individuals (all the old ones remain) • the old and the new then ‘fight’ for survival • In practice, any variation that you can think of!

  8. Other Selection Mechanisms • Proportional selection • calculate the fitness proportions as before • multiply the fitness proportion by the population size • round this to the nearest integer • that many of the individual survive • Tournament selection • to select a new population of size N, from N • repeat N times • pick two individuals at random from the population • the one with the highest fitness survives • supposedly mirrors natural competition more closely

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