— We present the memetic climber, a simple search algorithm that learns topology and weights of neural networks on different time scales. When applied to the problem of learning ...
— This paper presents a genetic algorithm (GA) with a stress-based crossover (SX) operator to obtain a solution without checkerboard patterns for multi-constrained topology optim...
— Since the Estimation of Distribution Algorithms (EDAs) have been introduced, several single model based EDAs and mixture model based EDAs have been developed. Take Gaussian mod...
—We present an approach for automated evolutionary design of the functionary of driving agent, able to operate a software model of fast running car. The objective of our work is ...
Hirotaka Yamazaki, Ivan Tanev, Tomoyuki Hiroyasu, ...
— Particle swarm has proven to be competitive to other evolutionary algorithms in the field of optimization, and in many cases enables a faster convergence to the ideal solution...
— Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a cost effe...
Dirk Gorissen, Luciano De Tommasi, Jeroen Croon, T...
— Solution diversity in evolutionary multi-objective optimization is considered. Although the Pareto front is ubiquitously used for the multi-objective optimization, the method o...
—This paper reviews the different gradient-based schemes and the sources of gradient, their availability, precision and computational complexity, and explores the benefits of usi...
Boyang Li, Yew-Soon Ong, Minh Nghia Le, Chi Keong ...