More than a decade after the first extensive overview on parameter control, we revisit the
field and present a survey of the state-of-the-art. We briefly summarize the development of …

Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide
range of problems. Their robustness, however, may be affected by several adjustable …

Chapter 4 GENETIC ALGORITHMS Page 1 Chapter 4 GENETIC ALGORITHMS Kumara Sastry,
David Goldberg University of Illinois, USA Graham Kendall University of Nottingham, UK 4.1 …

Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of …

Designing modular products can result in many benefits to both manufacturers and
consumers. The development of modular products requires the identification of highly …

In many different fields, researchers are often confronted by problems arising from complex
systems. Simple heuristics or even enumeration works quite well on small and easy …

Estimation of distribution algorithms (EDA s) guide the search for the optimum by building
and sampling explicit probabilistic models of promising candidate solutions. However, EDA …

This paper proposes a new evolutionary algorithm, called DSMGA-II, to efficiently solve
optimization problems via exploiting problem substructures. The proposed algorithm adopts …

We are living in the peta-byte era. We have larger and larger data to analyze, process and
transform into useful answers for the domain experts. Robust data mining tools, able to cope …

In the field of evolutionary algorithms (EAs), differential evolution (DE) has been the subject
of much attention due to its strong global optimization capability and simple implementation …