A significant challenge in genetic programming is premature convergence to local optima, which often prevents evolution from solving problems. This paper introduces to genetic pro...
One common characterization of how simple hill-climbing optimization methods can fail is that they become trapped in local optima - a state where no small modi cation of the curren...
GA-based clustering algorithms often employ either simple GA, steady state GA or their variants and fail to consistently and efficiently identify high quality solutions (best known...
NK models provide a family of tunably rugged fitness landscapes used in a wide range of evolutionary computation studies. It is well known that the average height of local optima...
Benjamin Skellett, Benjamin Cairns, Nicholas Geard...
The global search properties of heuristic search algorithms are not well understood. In this paper, we introduce a new metric, mobility, that quantifies the dispersion of local o...
Monte Lunacek, L. Darrell Whitley, James N. Knight
Constraint Satisfaction Problems can be solved using either iterative improvement or constructive search approaches. Iterative improvement techniques converge quicker than the cons...
The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group’s previous ...
We show how a random mutation hill climber that does multilevel selection utilizes transposition to escape local optima on the discrete Hierarchical-If-And-Only-If (HIFF) problem....
Abstract. Segmentation of images is often posed as a variational problem. As such, it is solved by formulating an energy functional depending on a contour and other image derived t...
A very undesirable behavior of any heuristic algorithm is to be stuck in some specific parts of the search space, in particular in the basins of attraction of the local optima. Wh...
Daniel Cosmin Porumbel, Jin-Kao Hao, Pascale Kuntz