Yoshiyuki Matsumura

Learn More
Evolution Strategies (ES) are an approach to numerical optimization that shows good optimization performance. However, it is found through our computer simulations that the performance changes with the lower bound of strategy parameters, although it has been overlooked in the ES community. We demonstrate that a population cannot practically move to other(More)
Parallel processing using graphic processing units (GPUs) have attracted much research interest in recent years. Parallel computation can be applied to genetic algorithms (GAs) in terms of the processes of individuals in a population. This paper describes the implementation of GAs in the compute unified device architecture (CUDA) environment. CUDA is a(More)
Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm inspired by the social behaviors of bird flocking and fish schooling. Each particle searches for a better solution through interaction with other particles. However, PSO tends to prematurely converge to a local minimum, particularly for large-scale multimodal problems.(More)
Evolutionary computation (EC) is well recognized as an effective method for solving many difficult optimization problems. However, EC generally entails large computational costs because it generally evaluates all solution candidates in a population for every generation. To overcome this disadvantage, parallel processing has been utilized such as in a form(More)
AbstractThe effect of noise on evolutionary dynamics of Evolutionary Programming(EP) is empirically observed using three different statistical values. These are (1) the averaged best function values, (2) the average of strategy parameters and (3) Hotelling’s T 2 of real values. The classical-EP(CEP), Fast-EP(FEP) and Robust-EP(REP) which stand for Fogel’s(More)