Hard problems for metaheuristic search can be a source of insight for developing better methods. We examine a challenging instance of such a problem that has exactly two local opt...
The field of evolutionary dynamic optimisation is concerned with the application of evolutionary algorithms to dynamic optimisation problems. In recent years, numerous new algorith...
We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. I...
Distributed constraint optimization (DCOP) is a promising approach to coordination, scheduling and task allocation in multi agent networks. In large-scale or low-bandwidth network...
Emma Bowring, Jonathan P. Pearce, Christopher Port...
The performance of genetic programming relies mostly on population-contained variation. If the population diversity is low then there will be a greater chance of the algorithm bein...
Abstract. We present upper bounds on the time and space complexity of finding the global optimum of additively separable functions, a class of functions that has been studied exten...
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...
— Deceptive problems are a class of challenging problems for conventional genetic algorithms (GAs), which usually mislead the search to some local optima rather than the global o...
Yang Chen, Jinglu Hu, Kotaro Hirasawa, Songnian Yu
In this paper, we rigorously analyse how the magnitude and frequency of change may affect the performance of the algorithm (1+1) EAdyn on a set of artificially designed pseudo-Bo...
We introduce a framework for computing statistically optimal estimates of geometric reconstruction problems. While traditional algorithms often suffer from either local minima or ...