In the AIPS98 Planning Contest, the HSP planner showed that heuristic search planners can be competitive with state-of-the-art Graphplan and SAT planners. Heuristic search planners like HSP transform planning problems into problems of heuristic search by automatically extracting heuristics from Strips encodings. They differ from specialized problem solvers such as those developed for the 24-Puz...

Memetic algorithms(MAs) represent one of the promising areas of evolutionary algorithms. However, there are many issues to be solved to design a robust MA. In this paper, we introduce an adaptive memetic algorithm, named GADEDHC, which combines a genetic algorithm and a differential evolution algorithm as global search methods with a directional hill climbing algorithm as local search method. I...

Recently, a hybrid methodology for combining genetic algorithms and local search algorithms has received considerable attention. This paper proposes an extended hybrid genetic algorithm to further improve the performance of finding the optimal solution in a large search space. Three key ideas, i.e. the elitism, nonredundant search, and steepest-ascent hill climbing, are introduced into a standa...

The problem of Bayesian network structure learning is defined as an optimization problem over the space of all possible network structures. For low-dimensional data, optimal structure learning approaches exist. For high-dimensional data, structure learning remains a significant challenge. Most commonly, approaches to high-dimensional structure learning employ a reduced search space and apply hi...

Recently, Tsamardinos et al. [2006] presented an algorithm for Bayesian network structure learning that outperforms many state-of-the-art algorithms in terms of efficiency, structure similarity and likelihood. The Max-Min Hill Climbing algorithm is a hybrid of constraint-based and search-and-score techniques, using greedy hill climbing to search a constrained space of possible network structure...

We analyze the behavior of hill-climbing algorithms for the minimum bisection problem on instances drawn from the \planted bisection" ran-is one of the few problem distributions for which various popular heuris-tic methods, such as simulated annealing, have been proven to succeed. However, it has been open whether these sophisticated methods were necessary , or whether simpler heuristics would ...

These last years two global optimizations methods hybridizing Evolutionary Algorithms (EA, but mainly GA) with hill-climbing methods have been investigated. The first one involves two interwoven levels of optimization: Evolution (EA) and Individual Learning (hill-climbing), which cooperate in the global optimization process. The second one consists of modifying EA by the introduction of new gen...

Adaptive search heuristics are known to be valuable in approximating solutions to hard search problems. However, these techniques are problem dependent. Inspired by the idea of life cycle stages found in nature, we introduce a hybrid approach called the LifeCycle model that simultaneously applies genetic algorithms (GAs), particle swarm optimisation (PSOs), and stochastic hill climbing to creat...

In this paper, motivations are presented to argue in favor of the affixal status of Romanian pronominal clitics. It will be suggested that they should not be considered lexical items, i.e. ‘signs’, which are located in a special position by rules of syntax, but a complex of syntactic and semantic information which is provided in the lexicon for the morphophonological realization of the cliticiz...