Image segmentation is a procedure of dividing the digital image into multiple set of pixels.
The intention of the segmentation is to “transform the representation of medical images into …

We present a theoretical framework for an asymptotically converging, scaled genetic
algorithm which uses an arbitrary-size alphabet and common scaled genetic operators. The …

Evolutionary algorithms (EAs) are random optimization methods inspired by genetics and
natural selection, resembling simulated annealing. We develop a method that can be used …

For large space dimensions, the log-linear convergence of the elitist evolution strategy with
a 1/5 success rule on the sphere fitness function has been observed, experimentally, from …

Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are
probabilistic algorithms for optimization, which mimic operators from natural selection and …

We propose two general stopping criteria for finite length, simple genetic algorithms based
on steady state distributions, and empirically investigate the impact of mutation rate, string …

Performance of evolutionary algorithms in real space is evaluated by local measures such
as success probability and expected progress. In high-dimensional landscapes, most …

In times of climate change, growing world population, and the resulting scarcity of resources,
efficient and economical usage of agricultural land is increasingly important and challenging …

Optimum well placement can help reservoir management teams in developing a field
development plan that could result in substantial increase in productivity and profitability of …

From a global viewpoint, evolutionary algorithms (EAs) working on continuous search-
spaces can be regarded as homogeneous Markov chains (MCs) with discrete time and …