Lots. For instance, in 1985 I used evolution to breed tic-tac-toe programs (in assembly on simulated game CPUs).
Koza used many different approaches, including evolving Lisp programs (the tree structure works well).
For most problem solving purposes simulated annealing works as well or better, with vastly less computation, which is perhaps the main reason that things like genetic programming have stayed niche rather than taking over the world.
Koza was also the one that first bred simulated creatures that learned to ambulate in a 3d environment, back in the late 80s, on Thinking Machines -- quite impressive in that era.
> the industry seems to only focus on neural nets.
There's more payoff per unit of computation. The right tool for the right job, and all that.
Lots. For instance, in 1985 I used evolution to breed tic-tac-toe programs (in assembly on simulated game CPUs).
Koza used many different approaches, including evolving Lisp programs (the tree structure works well).
For most problem solving purposes simulated annealing works as well or better, with vastly less computation, which is perhaps the main reason that things like genetic programming have stayed niche rather than taking over the world.
Koza was also the one that first bred simulated creatures that learned to ambulate in a 3d environment, back in the late 80s, on Thinking Machines -- quite impressive in that era.
> the industry seems to only focus on neural nets.
There's more payoff per unit of computation. The right tool for the right job, and all that.