Discriminating between secreted and membrane proteins is a challenging task. This is particularly true for discriminating between transmembrane segments and signal peptides because they have common biochemical properties. In this paper, we introduce a new predictive method called LSTranslocon (Local Search Translocon) based on a Local Search methodology. The method takes advantage of the latest...

Local search algorithms operate by making small changes to candidate solutions with the aim of reaching new and improved solutions. The problem is that often the search will become trapped at sub optimal states from where there are no improving neighbours. Much research has gone into creating schemes to avoid these local optima and various strategies exist mainly based around altering the accep...

This paper analyzes the detection of stagnation states in iterated local search algorithms. This is done considering elements such as the population size, the length of the encoding and the number of observed non-improving iterations. This analysis isolates the features of the target problem within one parameter for which three different estimations are given: two static a priori estimations an...

We investigate some ways in which massively parallel computing devices can be exploited in local search algorithms. We show that the substantial speed-ups that can be gained from parallel neighbourhood evaluation enables an efficient best improvement local search, and that this in turn enables further speed-ups through selection and parallel application of a set of independent, improving moves....

In this article, we present a new local method for multiobjective problems. It is an extension of local search algorithms for the single objective case, with specific mechanisms used to build the Pareto set. The performance of the local search algorithm is illustrated by experimental results based on a real problem with three objectives. The problem is issued from electric car-sharing service w...

One way to design a local search algorithm that is effective on many types of instances is allowing this algorithm to switch among heuristics. In this paper, we refer to the way in which non-weighting algorithm adaptGWSAT+ selects a variable to flip, as heuristic adaptGWSAT+, the way in which clause weighting algorithm RSAPS selects a variable to flip, as heuristic RSAPS, and the way in which v...

Abstract   Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...

For efficiency reasons, neighbourhoods in local search algorithms are often shrunk by only considering moves modifying variables that actually contribute to the overall penalty. These are known as conflicting variables. This is a well-known technique for speeding up search. State-of-the-art solutions to, e.g., the progressive party problem exploit this with great success. We propose a way of au...

A practical nurse rostering problem, which arises at a ward of an Italian hospital, is considered. The nurse rostering problem is a typical employee timetabling problem, where each month it is required to generate the nursing staff shifts subject to various contractual and operational requirements. These requirements may be in conflict especially for those months in which manpower is reduced du...