Landscape analysis under measurement error
There are situations where the need for optimisation with a global precision tolerance arises - for example, due to measurement, numerical or evaluation errors in the objective function. In such situations, a global tolerance ϵ > 0 can be predefined such that two objective values are declared equal if the absolute difference between them is less than or equal to ϵ. This paper presents an overview of fitness landscape analysis under such conditions. We describe the formulation of common landscape categories in the presence of a global precision tolerance. We then proceed by discussing issues that can emerge as a result of using tolerance, such as the increase in the neutrality of the fitness landscape. To this end, we propose two methods to exhaustively explore plateaus in such application domains - one of which is point-based and the other of which is set-based.
The Artificial Bee Colony (ABC) is a recently introduced swarm intelligence algorithm for optimization, that has previously been applied successfully to the training of neural networks.
The Artificial Bee Colony (ABC) is a swarm intelligence algorithm for optimization that has previously been applied to the training of neural networks.
This paper empirically studies basic properties of the fitness landscape of random instances of number partitioning problem, with a focus on how these properties change with the phase transition.…