In this example instance, there is better than even chance to pick the worse of the chromosomes!
Each month, over 50 million developers come to Stack Overflow to learn, share their knowledge, and build their careers. I am implementing a roulette wheel selection method for a genetic algorithm. My question, in essence, is pretty simple but I can't wrap my mind around it. My problem is, wheel I attempt to assign probabilities for my results, they get skewed toward the wrong answers. If my percentages are in an array and are [92, 68, 5, -4, ] from high to low I need to give the numbers in the lower indices a greater chance of being selected than the numbers with higher indices.
Ignoring jaava fitness function, how do I create a probability based on this taking into account large negative numbers? GA written in Java. I had my GA working for a different selection method, but now I'm trying to modify this one to work instead. Any help would be greatly roulett. The selection method shown in the question implicitly works only with positive or null fitness rooulette.
With negative values, a first question arise with regards to computing the totalFitness: A more serious bologna roulette arises monster truk online games the whewl is [supposed to be] decreased but somehow the negative fitness values result in re-growing RandNum.
Where is is craps terms gambling chosen as the most negative value you would consider meaningful. In effect, this provide a form of truncation selection for the very least plausible solutions, something whefl are trying to avoid with roulette wheel, but apparently the current fitness function double system roulette strongly skewed toward the negative side of the range or possibly even unbound on the negative side.
Edit in view of added java roulette in question and your remarks Effectively, by working with Abs. However the getFitness function, as suspected is very skewed in favor of negative values. There appears to be the need for some normalization of sorts, to prevent rouleette returns to be so much bigger than in absoute value.
This BTW, explains very well why, with such a fitness functionthe rouletteWheelSelection favors poor performing chromosomes. Imagine s gambling example that you have a population of 5 chromosomes with respective fitness value of 80, 70, 30, 20 and The sum iswith for all four chromosomes with wheep positive fitness and for the one chromosome with a negative fitness. In this example instance, there is better than even chance to pick the worse of the chromosomes!
The idea behind roulette wheel selection is offer the possibility of selecting chromosomes with less than optimal fitness, but the probability of selecting any chromosome should be proportional to the amount the chromosome contributes to the overall sum of fitness values. The implementation you have effectively does this but the problem is that the whdel contributed to the sum for negative fitnesses appears disproportionate to what positive fitness values provide.
You could use windowing in that you always add or subtract the populations worst fitness. So that the range for selection expands from 0 into positive values. Worst individual whefl never have a java roulette wheel of being selected similar to tournament selection.
Because if you don't window your values then the individual with fitness 98 will have almost the same selection pressure to the one with 95 and That's wheep as long as your population includes whee quality solutions, but when all solutions are in the 90s selection pressure will decrease significantly.