Trying to start a new life(Genetic Algorithms).....but those old memories(Deterministic Algorithms) still make me feel emotional.........

Joined JGAP to contribute to the opensource community. Genetic algorithms (GA's) are search algorithms that work via the process of natural selection. They begin with a sample set of potential solutions which then evolves toward a set of more optimal solutions. Within the sample set, solutions that are poor tend to die out while better solutions mate and propegate their advantageous traits, thus introducing more solutions into the set that boast greater potential (the total set size remains constant; for each new solution added, an old one is removed). A little random mutation helps guarantee that a set won't stagnate and simply fill up with numerous copies of the same solution. In general, genetic algorithms tend to work better than traditional optimization algorithms because they're less likely to be led astray by local optima. This is because they don't make use of single-point transition rules to move from one single instance in the solution space to another. Instead, GA's take advantage of an entire set of solutions spread throughout the solution space, all of which are experimenting upon many potential optima. However, in order for genetic algorithms to work effectively, a few criteria must be met: * It must be relatively easy to evaluate how "good" a potential solution is relative to other potential solutions. * It must be possible to break a potential solution into discrete parts that can vary independently. These parts become the "genes" in the genetic algorithm. * Finally, genetic algorithms are best suited for situations where a "good" answer will suffice, even if it's not the absolute best answer.