Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real valued optimization problems. Traditional investigations with differential evolution...
—Using semantic analysis, we present a technique known as semantically driven mutation which can explicitly detect and apply behavioural changes caused by the syntactic changes i...
— Despite their success as optimization methods, evolutionary algorithms face many difficulties to design artifacts with complex structures. According to paleontologists, living...
We describe a physico-chemical model relating measured fluorescence intensities on oligonucleotide microarrays to the underlying specific target concentration in the hybridized so...
— Many real-world problems demand a feasible solution to satisfy physical equilibrium, stability, or certain properties which require an additional lower level optimization probl...
— JADE is a recent variant of Differential Evolution (DE) for numerical optimization, which has been reported to obtain some promising results in experimental study. However, we ...
— The population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. Recently, the PBIL algorithm has been applied...
— The application of on-line learning techniques to modern computer games is a promising research direction. In fact, they can be used to improve the game experience and to achie...
Luigi Cardamone, Daniele Loiacono, Pier Luca Lanzi
— In this paper, we propose a new algorithm, named JACC-G, for large scale optimization problems. The motivation is to improve our previous work on grouping and adaptive weightin...
Zhenyu Yang, Jingqiao Zhang, Ke Tang, Xin Yao, Art...
—Developing systems that support people in everyday life in a discrete and effective way is an ultimate goal of a new generation of technical systems. Physiological computing rep...