Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local search-based metaheuristics to improve their behavior. This paper focuses on experimentally investigating the role of a recent approach, the binary-coded local genetic algorithm (BLGA), as context-independent local search o...

Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous local search algorithms that stand out as exceptional local search optimisers. However, on some occasions, they may become very expensive, because of the...

Memetic algorithms are evolutionary algorithms incorporating local search to increase exploitation. This hybridization has been fruitful in countless applications. However, theory on memetic algorithms is still in its infancy. Here, we introduce a simple memetic algorithm, the (1+1) Memetic Algorithm ((1+1) MA), working with a population size of 1 and no crossover. We compare it with the well-k...

In this paper, we propose an effective local search algorithm based on variable depth search (VDS) for the MCP. The VDS has been first successfully applied by Lin and Kernighan to the traveling salesman problem [5] and the graph partitioning problem [4]. Their algorithms are often called k-opt local search. The basic concept of the k-opt local search based on VDS is to search a portion of large...

Local search is a traditional technique to solve combinatorial search problems which has raised much interest in recent years. The design and implementation of local search algorithms is not an easy task in general and may require considerable experimentation and programming eeort. However, contrary to global search, little support is available to assist the design and implementation of local s...

Local search algorithms and iterated local search algorithms are a basic technique. Local search can be a stand along search methods, but it can also be hybridized with evolutionary algorithms. Recently, it has been shown that it is possible to identify improving moves in Hamming neighborhoods for k-bounded pseudo-Boolean optimization problems in constant time. This means that local search does...

Local search is a traditional technique to solve combinatorial search problems and has raised much interest in recent years. The design and implementation of local search algorithms is not an easy task in general and may require considerable experimentation and programming effort. However, contrary to global search, little support is available to assist the design and implementation of local se...

Local search is a traditional technique to solve combinatorial search problems which has raised much interest in recent years. The design and implementation of local search algorithms is not an easy task in general and may require considerable experimentation and programming eeort. However, contrary to global search, little support is available to assist the design and implementation of local s...

Local search is one of the fundamental paradigms for solving computationally hard combinatorial problems, including the constraint satisfaction problem (CSP). It provides the basis for some of the most successful and versatile methods for solving the large and difficult problem instances encountered in many real-life applications. Despite impressive advances in systematic, complete search algor...

The relatively recent appearance of high-dimensional databases has made traditional search algorithms too expensive in terms of time and memory resources. Thus, several modifications or enhancements to local search algorithms can be found in the literature to deal with this problem. However, nondeterministic global search, which is expected to perform better than local, still lacks appropriate ...