It is often suggested that traditional models of artificial evolution, based on explicit, human-defined fitness functions, are fundamentally more restricted and less creative than ...
We first revisit a problem in the literature of genetic algorithms: arranging numbers into groups whose summed weights are as nearly equal as possible. We provide a new genetic al...
Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function e...
Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied ...
Reinforcement learning algorithms that employ neural networks as function approximators have proven to be powerful tools for solving optimal control problems. However, their traini...