MACHINE LEARNING TECHNIQUES - LASA

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Often the crossover operator and selection method are too effective and they end up driving the
genetic algorithm to create a population of individuals that are almost exactly the same. When the
population consists of similar individuals, the likelihood of finding new solutions typically
decreases.
On one hand, you want the genetic algorithm to find good individuals, but on the other you want it
to maintain diversity. A common problem in optimization (not just genetic algorithms) is how to
define an objective function that accurately (and consistently) captures the effects of multiple
objectives.
In general, genetic algorithms are better than gradient search methods if your search space has
many local optima. Since the genetic algorithm traverses the search space using the genotype
rather than the phenotype, it is less likely to get stuck on a local high or low.
8.4 The Algorithm
In summary, the Genetic Algorithm goes as follows:
1. Define a basic population of chromosomes.
2. Evaluate the fitness of each chromosome.
3. Select parents with a probability (rank-based, roulette-wheel)
4. Breed pairs of parents (cross-over).
5. Apply mutation on children.
6. Evaluate fitness of children.
7. Population size is kept constant through a rejection process. Throw away the worst solutions of
the old population or of the old population + the new children.
8. Start again at point 3.
9. The GA stops when either satisfactory level of fitness is attained, or when the population is
uniform (local minima).
8.5 Convergence
There is, to this day, no theoretical proof that a GA will eventually converge, nor that it will
converge to the global optimum of the fitness function. There are, however, a number of “rules of
thumb” that one might follow to ensure good performance of the algorithm.
Convergence of a GA depends on choosing correctly the parameters. One should keep in mind
the following:
• If the mutation is too small (not frequent enough and making small steps), there will not be
enough exploration. Hence, the algorithm might get stuck in some local optimum.
• If, conversely, the mutation is too strong (too frequent and too important steps), the algorithm
will be very slow to converge.
• Cross-over can slow down convergence, by destroying good solutions.
• Convergence depends importantly on the encoding and on the location of the genes on the
chromosomes.
• The number of “children” produced at each generation determines the speed at which one
explores the environment.
© A.G.Billard 2004 – Last Update March 2011