Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Data mining
The theory of evolution strategies
The theory of evolution strategies
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Aided search strategy enabled by decision support
Information Processing and Management: an International Journal
A polynomial dynamic system approach to software design for attractivity requirement
Information Sciences: an International Journal
Information Sciences: an International Journal
Population size reduction for the differential evolution algorithm
Applied Intelligence
Research on using ANP to establish a performance assessment model for business intelligence systems
Expert Systems with Applications: An International Journal
Evolutionary adaptation of the differential evolution control parameters
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Aided search strategy enabled by decision support
Information Processing and Management: an International Journal
Self-adapting differential evolution algorithm with chaos random for global numerical optimization
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Alternative second-order cone programming formulations for support vector classification
Information Sciences: an International Journal
| Hi-index | 0.00 |
Self-adaptive software is one of the key discoveries in the field of evolutionary computation, originally invented in the framework of so-called Evolution Strategies in Germany. Self-adaptivity enables the algorithm to dynamically adapt to the problem characteristics and even to cope with changing environmental conditions - as they occur in unforeseeable ways in many real-world business applications.In evolution strategies, self-adaptability is generated by means of an evolutionary search process that operates on the solutions generated by the method as well as on the evolution strategy's parameters, i.e., the algorithm itself. By focusing on a basic algorithmic variant of evolution strategies, the fundamental idea of self-adaptation is outlined in this paper.Applications of evolution strategies for NuTech's clients include the whole range of business tasks, including R&D, technical design, control, production, quality control, logistics, and management decision support. While such examples can of course not be disclosed, we illustrate the capabilities of evolution strategies by giving some simpler application examples to problems occurring in traffic control and engineering.