Reinforcement learning problems are commonly tackled with temporal difference methods, which attempt to estimate the agent's optimal value function. In most real-world proble...
It has long been known that a fixed ordering of optimization phases will not produce the best code for every application. One approach for addressing this phase ordering problem ...
Prasad Kulkarni, Stephen Hines, Jason Hiser, David...
To increase the flexibility of single-chip evolvable hardware systems, we explore possibilities of systems with the evolutionary algorithm implemented in software on an onchip pr...
Kyrre Glette, Jim Torresen, Moritoshi Yasunaga, Yo...
We present discrete stochastic mathematical models for the growth curves of synchronous and asynchronous evolutionary algorithms with populations structured according to a random ...
Mario Giacobini, Marco Tomassini, Andrea Tettamanz...
We present the first hardware-in-the-loop evolutionary optimization on an ornithopter. Our experiments demonstrate the feasibility of evolving flight through genetic algorithms an...