Hybrid algorithms formed by the combination of Genetic Algorithms with Local Search methods provide increased performances when compared to real coded GA or Local Search alone. However, the cost of Local Search can be rather high. In this paper we present a new hybrid algorithm which reduces the total cost of local search by avoiding the start of the method in basins of attraction where a local...

We provide preliminary details and formulation of an optimization strategy under current development that is able to automatically tune the parameters of a Support Vector Machine over new datasets. The optimization strategy is a heuristic based on Iterated Local Search, a modification of classic hill climbing which iterates calls to a local search routine.

This paper proposes to use local search inside filtering algorithms of combinatorial structures for which achieving a desired level of consistency is too computationally expensive. Local search quickly provides supports for many variable-value pairs, thus reducing the effort required to check and potentially filter the rest of them. The idea is demonstrated on the SomeDifferent constraint, a gr...

Local Genetic Algorithms are search procedures designed in order to provide an effective local search. Several Genetic Algorithm models have recently been presented with this aim. In this paper we present a new Binary-coded Local Genetic Algorithm based on a Steady-State Genetic Algorithm with a crowding replacement method. We have compared a Multi-Start Local Search based on the Binary-Coded L...

Construction of cyclic staff schedules is of high practical relevance in a broad range of workplaces. Typically, such schedules must fulfill different hard constraints regarding the workforce requirements, sequences of shifts, and length of work and days-off blocks. We investigate the application of iterated local search to solve this problem. The current version of our algorithm has been exper...

We develop a method for computing efficient patient-specific drug protocols. Using this method, we identify two general categories of anticancer drug protocols, depending on the temporal cycle parameters of the host and cancer cells: a onetime intensive treatment, or a series of nonintensive treatments. Our method is based on a theoretical and experimental work showing that treatment efficacy c...

The solution of the biobjective IRP is rather challenging, even for metaheuristics. We are still lacking a profound understanding of appropriate solution representations and effective neighborhood structures. Clearly, both the delivery volumes and the routing aspects of the alternatives need to be reflected in an encoding, and must be modified when searching by means of local search. Our work c...

This paper proposes an effective genetic algorithm (GA) with inserting as well as removing mutation so as to solve the Orienteering Problem (OP). In order to obtain improved results we have taken into consideration not only the total profit but also the travel length for the given path. The computer experiments that have been conducted on a large transport network (approximately 900 vertices) p...

Local search is currently one of the most used methods for finding solution in real-life problems. In this paper we present an implementation of local search with Membrane Computing techniques applied to the N-queens problem as a case study. A CLIPS program inspired in the Membrane Computing design has been implemented and several experiments have been performed.

The recent Variable Neighborhood Search (VNS) metaheuristic combines local search with systematic changes of neighborhood in the descent and escape from local optimum phases. When solving large instances of various problems, its efficiency may be enhanced through decomposition. The resulting two level VNS, called Variable Neighborhood Decomposition Search (VNDS), is presented and illustrated on...