Geneettinen algoritmi päihittää älykkäät suunnittelijat

Olen palstalla aiemminkin kommentoinut aiheetta geneettiset algoritmit, jotka ovat yksi evoluutiotutkimuksen ja -teorian lukuisista sovelluksia ja ovat myös henkilökohtaisen mielenkiintoni kohde.

Brigham Young Univertisity:ssä on kehitty oppivaan hahmontunnistuksen geneettinen algoritmi, jonka avulla hahmontunnistus kykenee itsenäisesti luokittemaan hahmot ja tunnistamaan ne ilman, että ihminen määrittelee luokittelun perustana käytettäviä piirteitä ja muita parametrejä.

Kehitetyn geneettisen algoritmin ansiosta hahmontunnistus on tarkempaa ja luotettavampaa kuin tämän hetket parhaimmissa hahmontunnistusjärjestelmissä, missä ihminen määrittelee hahmoluokkien piirteet ja parametrit.

Tässä siis esimerkki siitä kuinka evoluution mekanismeja jäljittelevä geneettinen algoritmi päihittää älykkäät suunnittelijat ja uutta hyödyllistä informaatiota syntyy evoluution kautta :D

Kehitetystä geneettisestä algoritmista ja sen arvioinnista on raportoitu December issue of academic journal Pattern Recognition -julkaisussa.

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Lähde: http://news.byu.edu/archive14-jan-objectrecognition.aspx

BYU engineer Dah-Jye Lee has managed to eliminate the need for humans in the field of object recognition. Lee has created an algorithm that can accurately identify objects in images or video sequences without human calibration.

“In most cases, people are in charge of deciding what features to focus on and they then write the algorithm based off that,” said Lee, a professor of electrical and computer engineering. “With our algorithm, we give it a set of images and let the computer decide which features are important.”

Not only is Lee’s genetic algorithm able to set its own parameters, but it also doesn’t need to be reset each time a new object is to be recognized—it learns them on its own.

Lee likens the idea to teaching a child the difference between dogs and cats. Instead of trying to explain the difference, we show children images of the animals and they learn on their own to distinguish the two. Lee’s object recognition does the same thing: Instead of telling the computer what to look at to distinguish between two objects, they simply feed it a set of images and it learns on its own.

The BYU algorithm tested as well or better than other top object recognition algorithms to be published, including those developed by NYU’s Rob Fergus and Thomas Serre of Brown University.

Lee and his students fed their object recognition program four image datasets from CalTech (motorbikes, faces, airplanes and cars) and found 100 percent accurate recognition on every dataset. The other published well-performing object recognition systems scored in the 95-98% range.

The team has also tested their algorithm on a dataset of fish images from BYU’s biology department that included photos of four species: Yellowstone cutthroat, cottid, speckled dace and whitefish. The algorithm was able to distinguish between the species with 99.4% accuracy.

“It’s very comparable to other object recognition algorithms for accuracy, but, we don’t need humans to be involved,” Lee said. “You don’t have to reinvent the wheel each time. You just run it.”