This paper describes an iterative repair search method called constraint-based simulated annealing. Simulated annealing is a hill climbing search technique capable of escaping local minima. We demonstrate the utility of our constraint-based framework by comparing search performance with and without the constraint framework on a suite of randomly generated problems. We also show results of apply...

Binary descriptors of image patches provide processing speed advantages and require less storage than methods that encode the patch appearance with a vector of real numbers. We provide evidence that, despite its simplicity, a stochastic hill climbing descriptor construction process defeats recently proposed alternatives on a standard discriminative power benchmark. The method is easy to impleme...

We use directed search techniques in the space of computer programs to learn recursive sequences of positive integers. Specifically, the integer sequences of squares, x; cubes, x; factorial, x!; and Fibonacci numbers are studied. Given a small nite pre x of a sequence, we show that three directed searches|machine-language genetic programming with crossover, exhaustive iterative hill climbing, a...

Nearest neighbor search is known as a challenging issue that has been studied for several decades. Recently, this issue becomes more and more imminent in viewing that the big data problem arises from various fields. In this paper, a scalable solution based on hill-climbing strategy with the support of k-nearest neighbor graph (kNN) is presented. Two major issues have been considered in the pape...

Combinations of metaheuristics to solve highly constrained combinatorial optimisation problems have recently become a promising research area. Some combinations emerge from integrating evolutionary algorithms and local based algorithms to strike the right balance between global exploration of the search region and local exploitation of promising regions. This study presents hybridization betwee...

This paper reports on the use of search techniques to help optimise a case-based reasoning (CBR) system for predicting software project effort. A major problem, common to ML techniques in general, has been dealing with large numbers of case features, some of which can hinder the prediction process. Unfortunately searching for the optimal feature subset is a combinatorial problem and therefore N...

Several known heuristic functions can capture the input at different levels of precision, and support relaxation-refinement operations guaranteeing to converge to exact information in a finite number of steps. A natural idea is to use such refinement online, during search, yet this has barely been addressed. We do so here for local search, where relaxation refinement is particularly appealing: ...

Résumé en anglais Hill-climbing constitutes one of the simplest way to produce approximate solutions of a combinatorial optimization problem, and is a central component of most advanced metaheuristics. This paper focuses on evaluating climbing techniques in a context where deteriorating moves are not allowed, in order to isolate the intensification aspect of metaheuristics. We aim at providing ...

One of the basic elements in the development of the AI system is the search mechanism. The choice of the search method can determine the goodness of the developed system. In concrete, in the learning algorithms, the search mechanisms play a very important role. SLAVE is an inductive learning algorithm that describes the behavior of a system by a fuzzy rule set being a genetic algorithm of its s...