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I'm looking for a practical application to use a genetic algorithm for. Some things that have thought of are:
But none have really popped out at me. So if you had some free time (a few months) to spend on a genetic algorithms project, what would you choose to tackle?
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Problems which appear to be particularly appropriate for solution by genetic algorithms include timetabling and scheduling problems, and many scheduling software packages are based on GAs. GAs have also been applied to engineering.
The game-playing agent is built using only the genetic algorithm. The genetic algorithm itself is used to make decisions to tell where to move the player. There is no machine/deep learning model used. The mission of the game-playing agent is to collect all coins while avoiding collision with monsters and fire.
One topic with lots of possibilities is to use evolutionary algorithms to evolve strategies for game playing. People have used evolution to generate strategies for poker, checkers/draughts, Go and many other games. The J-GAP people have used genetic programming to evolve bots for Robocode.
I recently posted an introductory article about evolutionary computation. It includes details of some of the things evolutionary algorithms have been used for. Adam Marczyk has also written an excellent article with lots of examples. The Genetic Argonaut blog contains dozens of links to interesting evolutionary projects.
A less common type of evolutionary algorithm is the learning classifier system. This evolves a set of rules for classifying inputs. It can be applied to the same kind of problems that neural networks are used for. It could be interesting to develop an LCS for a particular problem, such as attempting to predict sports results based on form.
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You might be interested in something like Roger Alsing's Mona Lisa
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