Java roulette wheel selection
Hello, I'm trying to code a genetic algorithm in java but my code doesn't seem to be working as it should. I think the problem lies within my roulette wheel selection method. * candidate being assigned an area on a roulette wheel proportionate to its fitness * and the wheel being spun i times. Candidates may be selected more than * once. * * In some instances, particularly with small population sizes, the randomness * of selection may result in excessively high occurrences of particular candidates. * Selection * Tournament selection has become the primary selection technique used for * the Genetic Algorithm. Basic roulette wheel selection can be used, but * sometimes rank selection can be better. In general, elitism should be used * unless other method is used to save the best found solution. *.
Fitness proportionate selection
For the second parent, the same process is repeated. Therefore, the probability of choosing an individual depends directly on its fitness. The naive implementation is carried out by first generating the cumulative probability distribution CDF over the list of individuals using a probability proportional to the fitness of the individual. This could be achieved by dividing the fitness of a selection by the total fitness of all the selections, thereby normalizing them to 1. It takes in the O log n time to choose an individual. The higher ranked individuals are preferred more than the lower ranked ones. Maintaining good diversity in the population is extremely crucial for the success of a GA.
Genetic Algorithms - Parent Selection
Fitness proportionate selection , also known as roulette wheel selection , is a genetic operator used in genetic algorithms for selecting potentially useful solutions for recombination. In fitness proportionate selection, as in all selection methods, the fitness function assigns a fitness to possible solutions or chromosomes.
This fitness level is used to associate a probability of selection with each individual chromosome. This could be imagined similar to a Roulette wheel in a casino. Usually a proportion of the wheel is assigned to each of the possible selections based on their fitness value. This could be achieved by dividing the fitness of a selection by the total fitness of all the selections, thereby normalizing them to 1.
Then a random selection is made similar to how the roulette wheel is rotated. Contrast this with a less sophisticated selection algorithm, such as truncation selection , which will eliminate a fixed percentage of the weakest candidates.
With fitness proportionate selection there is a chance some weaker solutions may survive the selection process. This is because even though the probability that the weaker solutions will survive is low, it is not zero which means it is still possible they will survive; this is an advantage, because there is a chance that even weak solutions may have some features or characteristics which could prove useful following the recombination process. The analogy to a roulette wheel can be envisaged by imagining a roulette wheel in which each candidate solution represents a pocket on the wheel; the size of the pockets are proportionate to the probability of selection of the solution.
Parent Selection is the process of selecting parents which mate and recombine to create off-springs for the next generation. Parent selection is very crucial to the convergence rate of the GA as good parents drive individuals to a better and fitter solutions.
However, care should be taken to prevent one extremely fit solution from taking over the entire population in a few generations, as this leads to the solutions being close to one another in the solution space thereby leading to a loss of diversity. Maintaining good diversity in the population is extremely crucial for the success of a GA.
This taking up of the entire population by one extremely fit solution is known as premature convergence and is an undesirable condition in a GA. Fitness Proportionate Selection is one of the most popular ways of parent selection.
In this every individual can become a parent with a probability which is proportional to its fitness. Therefore, fitter individuals have a higher chance of mating and propagating their features to the next generation. Therefore, such a selection strategy applies a selection pressure to the more fit individuals in the population, evolving better individuals over time.
Consider a circular wheel. The wheel is divided into n pies , where n is the number of individuals in the population. Each individual gets a portion of the circle which is proportional to its fitness value. In a roulette wheel selection, the circular wheel is divided as described before. A fixed point is chosen on the wheel circumference as shown and the wheel is rotated. The region of the wheel which comes in front of the fixed point is chosen as the parent.
A way to beat the roulette was kept under wraps for decades - until now. Roulette seems random because of the way the ball bounces around before it comes to rest. The casino's "house" has an advantage of just a percentage point or two, and that's all that's necessary to come out ahead.
And, the complex and chaotic bouncing of the ball is what makes roulette a very difficult game to cheat. Until the s, when a man named Doyne Farmer came up with a way to beat the wheel.
He used an early computer to do the number crunching - a pretty notable feat then, since portable laptops wouldn't become common for another twenty years. But he didn't publish his findings because he didn't want to encourage cheating at casinos. They submitted a paper to the journal Chaos.
Farmer decided that there was no reason to keep quiet anymore. So how did they do it? It turns out that you don't have to follow the bouncing ball exactly.
Both methods are based on the motion of the ball as it leaves the dealer's hand and rolls around the side of the wheel.