What are good examples of genetic algorithms/genetic programming solutions? [closed]

Answer 1

First off, "Genetic Programming" by Jonathan Koza (on amazon) is pretty much THE book on genetic and evolutionary algorithm/programming techniques, with many examples. I highly suggest checking it out.

As for my own use of a genetic algorithm, I used a (home grown) genetic algorithm to evolve a swarm algorithm for an object collection/destruction scenario (practical purpose could have been clearing a minefield). Here is a link to the paper. The most interesting part of what I did was the multi-staged fitness function, which was a necessity since the simple fitness functions did not provide enough information for the genetic algorithm to sufficiently differentiate between members of the population.

Answer 2

Several years ago I used ga's to optimize asr (automatic speech recognition) grammars for better recognition rates. I started with fairly simple lists of choices (where the ga was testing combinations of possible terms for each slot) and worked my way up to more open and complex grammars. Fitness was determined by measuring separation between terms/sequences under a kind of phonetic distance function. I also experimented with making weakly equivalent variations on a grammar to find one that compiled to a more compact representation (in the end I went with a direct algorithm, and it drastically increased the size of the "language" that we could use in applications).

More recently I have used them as a default hypothesis against which to test the quality of solutions generated from various algorithms. This has largely involved categorization and different kinds of fitting problems (i.e. create a "rule" that explains a set of choices made by reviewers over a dataset(s)).

Answer 3

As part of my thesis I wrote a generic java framework for the multi-objective optimisation algorithm mPOEMS (Multiobjective prototype optimization with evolved improvement steps), which is a GA using evolutionary concepts. It is generic in a way that all problem-independent parts have been separated from the problem-dependent parts, and an interface is povided to use the framework with only adding the problem-dependent parts. Thus one who wants to use the algorithm does not have to begin from zero, and it facilitates work a lot.

You can find the code here.

The solutions which you can find with this algorithm have been compared in a scientific work with state-of-the-art algorithms SPEA-2 and NSGA, and it has been proven that the algorithm performes comparable or even better, depending on the metrics you take to measure the performance, and especially depending on the optimization-problem you are looking on.

You can find it here.

Also as part of my thesis and proof of work I applied this framework to the project selection problem found in portfolio management. It is about selecting the projects which add the most value to the company, support most the strategy of the company or support any other arbitrary goal. E.g. selection of a certain number of projects from a specific category, or maximization of project synergies, ...

My thesis which applies this framework to the project selection problem: http://www.ub.tuwien.ac.at/dipl/2008/AC05038968.pdf

After that I worked in a portfolio management department in one of the fortune 500, where they used a commercial software which also applied a GA to the project selection problem / portfolio optimization.

Further resources:

The documentation of the framework: http://thomaskremmel.com/mpoems/mpoems_in_java_documentation.pdf

mPOEMS presentation paper: http://portal.acm.org/citation.cfm?id=1792634.1792653

Actually with a bit of enthusiasm everybody could easily adapt the code of the generic framework to an arbitrary multi-objective optimisation problem.

Answer 4

I developed a multithreaded swing based simulation of robot navigation through a set of randomized grid terrain of food sources and mines and developed a genetic algorithm based strategy of exploring the optimization of robotic behavior and survival of fittest genes for a robotic chromosome. This was done using charting and mapping of each iteration cycle.

Since, then I have developed even more game behavior. An example application I built recently for myself was a genetic algorithm for solving the traveling sales man problem in route finding in UK taking into account start and goal states as well as one/multiple connection points, delays, cancellations, construction works, rush hour, public strikes, consideration between fastest vs cheapest routes. Then providing a balanced recommendation for the route to take on a given day.

Generally, my strategy is to use POJO based representaton of genes then I apply specific interface implementations for selection, mutation, crossover strategies, and the criteria point. My fitness function then basically becomes a quite complex based on the strategy and criteria I need to apply as a heuristic measure.

I have also looked into applying genetic algorithm into automated testing within code using systematic mutation cycles where the algorithm understands the logic and tries to ascertain a bug report with recommendations for code fixes. Basically, a way to optimize my code and provide recommendations for improvement as well as a way of automating the discovery of new programmatic code. I have also tried to apply genetic algorithms to music production amongst other applications.

Generally, I find evolutionary strategies like most metaheuristic/global optimization strategies, they are slow to learn at first but start to pick up as the solutions become closer and closer to goal state and as long as your fitness function and heuristics are well aligned to produce that convergence within your search space.

Answer 5

I used a GA to optimize seating assignments at my wedding reception. 80 guests over 10 tables. Evaluation function was based on keeping people with their dates, putting people with something in common together, and keeping people with extreme opposite views at separate tables.

I ran it several times. Each time, I got nine good tables, and one with all the odd balls. In the end, my wife did the seating assignments.

My traveling salesman optimizer used a novel mapping of chromosome to itinerary, which made it trivial to breed and mutate the chromosomes without any risk of generating invalid tours.

Update: Because a couple people have asked how ...

Start with an array of guests (or cities) in some arbitrary but consistent ordering, e.g., alphabetized. Call this the reference solution. Think of a guest's index as his/her seat number.

Instead of trying to encode this ordering directly in the chromosome, we encode instructions for transforming the reference solution into a new solution. Specifically, we treat the chromosomes as lists of indexes in the array to swap. To get decode a chromosome, we start with the reference solution and apply all the swaps indicated by the chromosome. Swapping two entries in the array always results in a valid solution: every guest (or city) still appears exactly once.

Thus chromosomes can be randomly generated, mutated, and crossed with others and will always produce a valid solution.

Answer 6

As part of my undergraduate CompSci degree, we were assigned the problem of finding optimal jvm flags for the Jikes research virtual machine. This was evaluated using the Dicappo benchmark suite which returns a time to the console. I wrote a distributed gentic alogirthm that switched these flags to improve the runtime of the benchmark suite, although it took days to run to compensate for hardware jitter affecting the results. The only problem was I didn't properly learn about the compiler theory (which was the intent of the assignment).

I could have seeded the initial population with the exisiting default flags, but what was interesting was that the algorithm found a very similar configuration to the O3 optimisation level (but was actually faster in many tests).

Edit: Also I wrote my own genetic algorithm framework in Python for the assignment, and just used the popen commands to run the various benchmarks, although if it wasn't an assessed assignment I would have looked at pyEvolve.