Simulated evolution by the use of Genetic Algorithms (GA) is presented as the solution to a twofaceted problem: the challenge for an autonomous agent to learn the reactive componen...
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
We present an optimization algorithm based on a model of bacterial chemotaxis. The original biological model is used to formulate a simple optimization algorithm, which is evaluate...
Inspired by Darwinian evolution, a genetic algorithm (GA) approach is one of the popular heuristic methods for solving hard problems, such as the Job Shop Scheduling Problem (JSSP...
Evolution of quantum circuits faces two major challenges: complex and huge search spaces and the high costs of simulating quantum circuits on conventional computers. In this paper ...