What are good examples of genetic algorithms genetic programming solutions

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Genetic algorithms  GA  and genetic programming  GP  are interesting areas of research    I d like to know about specific problems you have solved using GA GP and what libraries frameworks you used if you didn t roll your own   Questions    What problems have you used GA GP to solve  What libraries frameworks did you use    I m looking for first-hand experiences  so please do not answer unless you have that

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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

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After reading The Blind Watchmaker  I was interested in the pascal program Dawkins said he had developed to create models of organisms that could evolve over time   I was interested enough to write my own using Swarm   I didn t make all the fancy critter graphics he did  but my  chromosomes  controlled traits which affected organisms ability to survive   They lived in a simple world and could slug it out against each other and their environment     Organisms lived or died partly due to chance  but also based on how effectively they adapted to their local environments  how well they consumed nutrients  amp  how successfully they reproduced   It was fun  but also more proof to my wife that I am a geek

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For my undergrad thesis I used Genetic Programming to develop cooperative search strategies to be used for aerial search and rescue  I used an open source agent modelling platform called NetLogo  based on StarLogo  as the world model  NetLogo is written in java and thus provides java APIs - so the GP framework needed to be based on java - the one I used is called JGAP there is also another open source GP framework in java I know of called ECJ   The simulations were quite slow to run  I think this is due to the NetLogo model  so my function terminal sets were quite restricted  limiting the search space t Despite this  I came up with some good solutions  If you feel the urge  you can read about it in chapter 3 of my thesis http   www cse unsw edu au  ekjo014 z3157867 Thesis pdf

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A coworker and I are working on a solution for loading freight onto trucks using the various criteria our company requires   I ve been working on a Genetic Algorithm solution while he is using a Branch And Bound with aggressive pruning   We are still in the process of implementing this solution but so far  we have been getting good results

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Evolutionary Computation Graduate Class  Developed a solution for TopCoder Marathon Match 49  MegaParty  My small group was testing different domain representations and how the different representation would affect the ga s ability to find the correct answer  We rolled our own code for this problem   Neuroevolution and Generative and Developmental Systems  Graduate Class  Developed an Othello game board evaluator that was used in the min-max tree of a computer player  The player was set to evaluate one-deep into the game  and trained to play against a greedy computer player that considered corners of vital importance  The training player saw either 3 or 4 deep  I ll need to look at my config files to answer  and they re on a different computer   The goal of the experiment was to compare Novelty Search to traditional  fitness-based search in the Game Board Evaluation domain  Results were relatively inconclusive  unfortunately  While both the novelty search and fitness-based search methods came to a solution  showing that Novelty Search can be used in the Othello domain   it was possible to have a solution to this domain with no hidden nodes  Apparently I didn t create a sufficiently competent trainer if a linear solution was available  and it was possible to have a solution right out of the gates   I believe my implementation of Fitness-based search produced solutions more quickly than my implementation of Novelty search  this time   this isn t always the case   Either way  I used ANJI   Another NEAT Java Implementation  for the neural network code  with various modifications  The Othello game I wrote myself

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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

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in undergrad  we used NERO  a combination of neural network and genetic algorithm  to teach in-game robots to make intelligent decisions  It was pretty cool

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I made a little critters that lived in this little world  They had a neural network brain which received some inputs from the world and the output was a vector for movement among other actions  Their brains were the  genes     The program started with a random population of critters with random brains  The inputs and output neurons were static but what was in between was not    The environment contained food and dangers  Food increased energy and when you have enough energy  you can mate  The dangers would reduce energy and if energy was 0  they died    Eventually the creatures evolved to move around the world and find food and avoid the dangers    I then decided to do a little experiment  I gave the creature brains an output neuron called  mouth  and an input neuron called  ear   Started over and was surprised to find that they evolved to maximize the space and each respective creature would stay in its respective part  food was placed randomly   They learned to cooperate with each other and not get in each others way  There were always the exceptions   Then i tried something interesting  I dead creatures would become food  Try to guess what happened  Two types of creatures evolved  ones that attacked like in swarms  and ones that were high avoidance    So what is the lesson here  Communication means cooperation  As soon as you introduce an element where hurting another means you gain something  then cooperation is destroyed    I wonder how this reflects on the system of free markets and capitalism  I mean  if businesses can hurt their competition and get away with it  then its clear they will do everything in their power to hurt the competition    Edit    I wrote it in C   using no frameworks  Wrote my own neural net and GA code  Eric  thank you for saying it is plausible  People usually don t believe in the powers of GA  although the limitations are obvious  until they played with it  GA is simple but not simplistic   For the doubters  neural nets have been proven to be able to simulate any function if they have more than one layer  GA is a pretty simple way to navigate a solution space finding local and potentially global minimum  Combine GA with neural nets and you have a pretty good way to find functions that find approximate solutions for generic problems  Because we are using neural nets  then we are optimizing the function for some inputs  not some inputs to a function as others are using GA  Here is the demo code for the survival example  http   www mempko com darcs neural demos eaters  Build instructions    Install darcs  libboost  liballegro  gcc  cmake  make darcs clone --lazy http   www mempko com darcs neural  cd neural cmake   make cd demos eaters   eaters

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Not homework   My first job as a professional programmer  1995  was writing a genetic-algorithm based automated trading system for S amp P500 futures   The application was written in Visual Basic 3     and I have no idea how I did anything back then  since VB3 didn t even have classes   The application started with a population of randomly-generated fixed-length strings  the  gene  part   each of which corresponded to a specific shape in the minute-by-minute price data of the S amp P500 futures  as well as a specific order  buy or sell  and stop-loss and stop-profit amounts   Each string  or  gene   had its profit performance evaluated by a run through 3 years of historical data  whenever the specified  shape  matched the historical data  I assumed the corresponding buy or sell order and evaluated the trade s result   I added the caveat that each gene started with a fixed amount of money and could thus potentially go broke and be removed from the gene pool entirely   After each evaluation of a population  the survivors were cross-bred randomly  by just mixing bits from two parents   with the likelihood of a gene being selected as a parent being proportional to the profit it produced   I also added the possibility of point mutations to spice things up a bit   After a few hundred generations of this  I ended up with a population of genes that could turn  5000 into an average of about  10000 with no chance of death brokeness  on the historical data  of course    Unfortunately  I never got the chance to use this system live  since my boss lost close to  100 000 in less than 3 months trading the traditional way  and he lost his willingness to continue with the project   In retrospect  I think the system would have made huge profits - not because I was necessarily doing anything right  but because the population of genes that I produced happened to be biased towards buy orders  as opposed to sell orders  by about a 5 1 ratio   And as we know with our 20 20 hindsight  the market went up a bit after 1995

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I built a simple GA for extracting useful patterns out of the frequency spectrum of music as it was being played  The output was used to drive graphical effects in a winamp plugin    Input  a few FFT frames  imagine a 2D array of floats  Output  single float value  weighted sum of inputs   thresholded to 0 0 or 1 0 Genes  input weights Fitness function  combination of duty cycle  pulse width and BPM within sensible range    I had a few GAs tuned to different parts of the spectrum as well as different BPM limits  so they didn t tend to converge towards the same pattern  The outputs from the top 4 from each population were sent to the rendering engine   An interesting side effect was that the average fitness across the population was a good indicator for changes in the music  although it generally took 4-5 seconds to figure it out

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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

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I experimented with GA in my youth   I wrote a simulator in Python that worked as follows   The genes encoded the weights of a neural network   The neural network s inputs were  antennae  that detected touches   Higher values meant very close and 0 meant not touching   The outputs were to two  wheels    If both wheels went forward  the guy went forward   If the wheels were in opposite directions  the guy turned   The strength of the output determined the speed of the wheel turning   A simple maze was generated   It was really simple--stupid even   There was the start at the bottom of the screen and a goal at the top  with four walls in between   Each wall had a space taken out randomly  so there was always a path   I started random guys  I thought of them as bugs  at the start   As soon as one guy reached the goal  or a time limit was reached  the fitness was calculated   It was inversely proportional to the distance to the goal at that time   I then paired them off and  bred  them to create the next generation   The probability of being chosen to be bred was proportional to its fitness   Sometimes this meant that one was bred with itself repeatedly if it had a very high relative fitness   I thought they would develop a  left wall hugging  behavior  but they always seemed to follow something less optimal   In every experiment  the bugs converged to a spiral pattern   They would spiral outward until they touched a wall to the right   They d follow that  then when they got to the gap  they d spiral down  away from the gap  and around   They would make a 270 degree turn to the left  then usually enter the gap   This would get them through a majority of the walls  and often to the goal   One feature I added was to put in a color vector into the genes to track relatedness between individuals   After a few generations  they d all be the same color  which tell me I should have a better breeding strategy   I tried to get them to develop a better strategy   I complicated the neural net--adding a memory and everything   It didn t help   I always saw the same strategy   I tried various things like having separate gene pools that only recombined after 100 generations   But nothing would push them to a better strategy   Maybe it was impossible   Another interesting thing is graphing the fitness over time   There were definite patterns  like the maximum fitness going down before it would go up   I have never seen an evolution book talk about that possibility

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I once used a GA to optimize a hash function for memory addresses   The addresses were 4K or 8K page sizes  so they showed some predictability in the bit pattern of the address  least significant bits all zero  middle bits incrementing regularly  etc    The original hash function was  chunky  - it tended to cluster hits on every third hash bucket   The improved algorithm had a nearly perfect distribution

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In a seminar in the school  we develop an application to generate music based in the musical mode  The program was build in Java and the output was a midi file with the song  We using distincts aproachs of GA to generate the music  I think this program can be useful to explore new compositions

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At work I had the following problem   given M tasks and N DSPs  what was the best way to assign tasks to DSPs    Best  was defined as  minimizing the load of the most loaded DSP    There were different types of tasks  and various task types had various performance ramifications depending on where they were assigned  so I encoded the set of job-to-DSP assignments as a  DNA string  and then used a genetic algorithm to  breed  the best assignment string I could   It worked fairly well  much better than my previous method  which was to evaluate every possible combination    on non-trivial problem sizes  it would have taken years to complete    the only problem was that there was no way to tell if the optimal solution had been reached or not   You could only decide if the current  best effort  was good enough  or let it run longer to see if it could do better

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I once tried to make a computer player for the game of Go  exclusively based on genetic programming   Each program would be treated as an evaluation function for a sequence of moves   The programs produced weren t very good though  even on a rather diminuitive 3x4 board   I used Perl  and coded everything myself   I would do things differently today

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As well as some of the common problems  like the Travelling Salesman and a variation on Roger Alsing s Mona Lisa program  I ve also written an evolutionary Sudoku solver  which required a bit more original thought on my part  rather than just re-implementing somebody else s idea    There are more reliable algorithms for solving Sudokus but the evolutionary approach works fairly well   In the last few days I ve been playing around with an evolutionary program to find  cold decks  for poker after seeing this article on Reddit   It s not quite satisfactory at the moment but I think I can improve it   I have my own framework that I use for evolutionary algorithms

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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

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I built this little fun doodad a few weeks ago  It generates funny internet images using a GA  Kinda dumb but good for a laugh   http   www twitterandom info GAFunny   Some insight into this  It is a few mysql tables  One for the list of images and their score  which is the fitness  and another for the sub-images and their locations on the page   Sub-images can have several details  not all implemented   size  skew  rotation   location   image url   As people vote on how funny the image is  it is more or less likely to survive to the next generation  If it survives  it produces 5-10 offspring with slight mutations  There is no crossover yet

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I don t know if homework counts     During my studies we rolled our own program to solve the Traveling Salesman problem   The idea was to make a comparison on several criteria  difficulty to map the problem  performance  etc  and we also used other techniques such as Simulated annealing   It worked pretty well  but it took us a while to understand how to do the  reproduction  phase correctly  modeling the problem at hand into something suitable for Genetic programming really struck me as the hardest part     It was an interesting course since we also dabbled with neural networks and the like   I d like to know if anyone used this kind of programming in  production  code

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In January 2004  I was contacted by Philips New Display Technologies who were creating the electronics for the first ever commercial e-ink  the Sony Librie  who had only been released in Japan  years before Amazon Kindle and the others hit the market in US an Europe   The Philips engineers had a major problem  A few months before the product was supposed to hit the market  they were still getting ghosting on the screen when changing pages  The problem was the 200 drivers that were creating the electrostatic field  Each of these drivers had a certain voltage that had to be set right between zero and 1000 mV or something like this  But if you changed one of them  it would change everything    So optimizing each driver s voltage individually was out of the question  The number of possible combination of values was in billions and it took about 1 minute for a special camera to evaluate a single combination  The engineers had tried many standard optimization techniques  but nothing would come close    The head engineer contacted me because I had previously released a Genetic Programming library to the open-source community  He asked if GP GA s would help and if I could get involved  I did  and for about a month we worked together  me writing and tuning the GA library  on synthetic data  and him integrating it into their system  Then  one weekend they let it run live with the real thing   The following Monday I got these glowing emails from him and their hardware designer  about how nobody could believe the amazing results the GA found  This was it  Later that year the product hit the market   I didn t get paid one cent for it  but I got  bragging  rights  They said from the beginning they were already over budget  so I knew what the deal was before I started working on it  And it s a great story for applications of GAs

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It was a while ago  but I rolled a GA to evolve what were in effect image processing kernels to remove cosmic ray traces from Hubble Space Telescope  HST  images  The standard approach is to take multiple exposures with the Hubble and keep only the stuff that is the same in all the images  Since HST time is so valuable  I m an astronomy buff  and had recently attended the Congress on Evolutionary Computation  I thought about using a GA to clean up single exposures    The individuals were in the form of trees that took a 3x3 pixel area as input  performed some calculations  and produced a decision about whether and how to modify the center pixel  Fitness was judged by comparing the output with an image cleaned up in the traditional way  i e  stacking exposures    It actually sort of worked  but not well enough to warrant foregoing the original approach  If I hadn t been time-constrained by my thesis  I might have expanded the genetic parts bin available to the algorithm  I m pretty sure I could have improved it significantly   Libraries used  If I recall correctly  IRAF and cfitsio for astronomical image data processing and I O

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I used a simple genetic algorithm to optimize the signal to noise ratio of a wave that was represented as a binary string  By flipping the the bits certain ways over several million generations I was able to produce a transform that resulted in a higher signal to noise ratio of that wave  The algorithm could have also been  Simulated Annealing  but was not used in this case  At their core  genetic algorithms are simple  and this was about as simple of a use case that I have seen  so I didn t use a framework for generation creation and selection - only a random seed and the Signal-to-Noise Ratio function at hand

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Its often difficult to get an exact color combination when you are planning to paint your house  Often  you have some color in mind  but it is not one of the colors  the vendor shows you    Yesterday  my Prof  who is a GA researcher mentioned about a true story in Germany  sorry  I have no further references  yes  I can find it out if any one requests to   This guy  let s call him the color guy  used to go from door-door to help people to find the exact color code  in RGB  that would be the closet to what the customer had in mind  Here is how he would do it   The color guy used to carry with him a software program which used GA  He used to start with 4 different colors- each coded as a coded Chromosome  whose decoded value would be a RGB value   The consumer picks 1 of the 4 colors  Which is the closest to which he she has in mind   The program would then assign the maximum fitness to that individual and move onto the next generation using mutation crossover  The above steps would be repeated till the consumer had found the exact color and then color guy used to tell him the RGB combination    By assigning maximum fitness to the color closes to what the consumer have in mind  the color guy s program is increasing the chances to converge to the color  the consumer has in mind exactly  I found it pretty fun   Now that I have got a -1  if you are planning for more -1 s  pls  elucidate the reason for doing so

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In 2007-9 I developed some software for reading datamatrix patterns   Often these patterns were difficult to read  being indented into scratched surfaces with all kinds of reflectance properties  fuzzy chemically etched markings and so on   I used a GA to fine tune various parameters of the vision algorithms to give the best results on a database of 300 images having known properties   Parameters were things like downsampling resolution  RANSAC parameters  amount of erosion and dilation  low pass filtering radius  and a few others   Running the optimisation over several days this produced results which were about 20  better than naive values on a test set of images unseen during the optimisation phase   This system was completely written from scratch  and I didn t use any other libraries   I m not opposed to using such things  provided that they give a reliable result  but you have to be careful about license compatibility and code portability issues

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There was an competition on codechef com  great site by the way  monthly programming competitions  where one was supposed to solve an unsolveable sudoku  one should come as close as possible with as few wrong collumns rows etc as possible  What I would do  was to first generate a perfect sudoku and then override the fields  that have been given  From this pretty good basis on I used genetic programming to improve my solution I couldn t think of a deterministic approach in this case  because the sudoku was 300x300 and search would ve taken too long

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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

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I developed a home brew GA for a 3D laser surface profile system my company developed for the freight industry back in 1992  The system relied upon 3 dimensional triangulation and used a custom laser line scanner  a 512x512 camera  with custom capture hw   The distance between the camera and laser was never going to be precise and the focal point of the cameras were not to be found in the 256 256 position that you expected it to be   It was a nightmare to try and work out the calibration parameters using standard geometry and simulated annealing style equation solving   The Genetic algorithm was whipped up in an evening and I created a calibration cube to test it on  I knew the cube dimensions to high accuracy and thus the idea was that my GA could evolve a set of custom triangulation parameters for each scanning unit that would overcome production variations   The trick worked a treat  I was flabbergasted to say the least  Within around 10 generations my  virtual  cube  generated from the raw scan and recreated from the calibration parameters  actually looked like a cube  After around 50 generations I had the calibration I needed

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Football Tipping   I built a GA system to predict the week to week outcome of games in the AFL  Aussie Rules Football    A few years ago I got bored of the standard work football pool  everybody was just going online and taking the picks from some pundit in the press   So  I figured it couldn t be too hard to beat a bunch of broadcast journalism majors  right   My first thought was to take the results from Massey Ratings and then reveal at the end of the season my strategy after winning fame and glory   However  for reasons I ve never discovered Massey does not track AFL   The cynic in me believes it is because the outcome of each AFL game has basically become random chance  but my complaints of recent rule changes belong in a different forum   The system basically considered offensive strength  defensive strength  home field advantage  week to week improvement  or lack thereof  and velocity of changes to each of these   This created a set of polynomial equations for each team over the season   The winner and score for each match for a given date could be computed   The goal was to find the set of coefficients that most closely matched the outcome of all past games and use that set to predict the upcoming weeks game   In practice  the system would find solutions that accurately predicted over 90  of past game outcomes   It would then successfully pick about 60-80  of games for the upcoming week  that is the week not in the training set    The result  just above middle of the pack   No major cash prize nor a system that I could use to beat Vegas   It was fun though   I built everything from scratch  no framework used

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I made a complete GA framework named  GALAB   to solve many problems    locating GSM ANTs  BTS  to decrease overlap  amp  blank locations  Resource constraint project scheduling  Evolutionary picture creation   Evopic  Travelling salesman problem  N-Queen  amp  N-Color problems  Knight s tour  amp  Knapsack problems  Magic square  amp  Sudoku puzzles  string compression  based on Superstring problem  2D Packaging problem  Tiny artificial life APP  Rubik puzzle

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I used genetic algorithms  as well as some related techniques  to determine the best settings for a risk management system that tried to keep gold farmers from using stolen credit cards to pay for MMOs   The system would take in several thousand transactions with  known  values  fraud or not  and figure out what the best combination of settings was to properly identify the fraudulent transactions without having too many false positives   We had data on several dozen  boolean  characteristics of a transaction  each of which was given a value and totalled up   If the total was higher than a threshold  the transaction was fraud   The GA would create a large number of random sets of values  evaluate them against a corpus of known data  select the ones that scored the best  on both fraud detection and limiting the number of false positives   then cross breed the best few from each generation to produce a new generation of candidates   After a certain number of generations the best scoring set of values was deemed the winner   Creating the corpus of known data to test against was the Achilles  heel of the system   If you waited for chargebacks  you were several months behind when trying to respond to the fraudsters  so someone would have to manually review large numbers of transactions to build up that corpus of data without having to wait too long   This ended up identifying the vast majority of the fraud that came in  but couldn t quite get it below 1  on the most fraud-prone items  given that 90  of incoming transactions could be fraud  that was doing pretty well    I did all this using perl   One run of the software on a fairly old linux box would take 1-2 hours to run  20 minutes to load data over a WAN link  the rest of the time spent crunching    The size of any given generation was limited by available RAM   I d run it over and over with slight changes to the parameters  looking for an especially good result set   All in all it avoided some of the gaffes that came with manually trying to tweak the relative values of dozens of fraud indicators  and consistently came up with better solutions than I could create by hand   AFAIK  it s still in use  about 3 years after I wrote it

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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

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I am part of a team investigating the use of Evolutionary Computation  EC  to automatically fix bugs in existing programs   We have successfully repaired a number of real bugs in real world software projects  see this project s homepage    We have two applications of this EC repair technique    The first  code and reproduction information available through the project page  evolves the abstract syntax trees parsed from existing C programs and is implemented in Ocaml using our own custom EC engine  The second  code and reproduction information available through the project page   my personal contribution to the project  evolves the x86 assembly or Java byte code compiled from programs written in a number of programming languages   This application is implemented in Clojure and also uses its own custom built EC engine    One nice aspect of Evolutionary Computation is the simplicity of the technique makes it possible to write your own custom implementations without too much difficulty   For a good freely available introductory text on Genetic Programming see the Field Guide to Genetic Programming

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A couple of weeks ago  I suggested a solution on SO using genetic algorithms to solve a problem of graph layout  It is an example of a constrained optimization problem   Also in the area of machine learning  I implemented a GA-based classification rules framework in c c   from scratch  I ve also used GA in a sample project for training artificial neural networks  ANN  as opposed to using the famous backpropagation algorithm   In addition  and as part of my graduate research  I ve used GA in training Hidden Markov Models as an additional approach to the EM-based Baum-Welch algorithm  in c c   again

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

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