The coarse-to-fine search strategy is extensively used in current reported research. However, it has the same problems as any hill climbing algorithm, most importantly, it often finds local instead of global minima. Drawing upon the artificial intelligence literature, we applied an optimal graph search, namely A*, to the problem. Using real stereo and video test sets, we compared the A* method ...

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of ...

This paper describes the meta-level control system of a program (Dominic) for parametric design of mechanical components by iterative redesign. We view parametric design as search, and thus Dominic is a hill climbing algorithm. However, from experience with Dominic we concluded that modeling engineering design as hill climbing has several limitations. Therefore, a need for meta-level control kn...

Bayesian networks are frequently used to model statistical dependencies in data. Without prior knowledge of dependencies in the data, the structure of a Bayesian network is learned from the data. Bayesian network structure learning is commonly posed as an optimization problem where search is used to find structures that maximize a scoring function. Since the structure search space is superexpon...

We present an information-theoretic analysis of Darwin’s theory of evolution, modeled as a hill-climbing algorithm on a fitness landscape. Our space of possible organisms consists of computer programs, which are subjected to random mutations. We study the random walk of increasing fitness made by a single mutating organism. In two different models we are able to show that evolution will occur a...

We present an information-theoretic analysis of Darwin’s theory of evolution, modeled as a hill-climbing algorithm on a fitness landscape. Our space of possible organisms consists of computer programs, which are subjected to random mutations. We study the random walk of increasing fitness made by a single mutating organism. In two different models we are able to show that evolution will occur a...

We address the problem of completing two files with records containing a common subset of variables. The technique investigated involves the use of regression and/or classification trees. An extension (the “forest-climbing” algorithm) is proposed to deal with multivariate response variables. The method is demonstrated on a real problem, in which two surveys are merged, and shown to be feasible ...

This paper presents new results showing that a very simple stochastic hill climbing algorithm is as good or better than more complex metaheuristic methods for solving an oversubscribed scheduling problem: scheduling communication contacts on the Air Force Satellite Control Network (AFSCN). The empirical results also suggest that the best neighborhood construction choices produce a search that i...

When using Simple Temporal Networks, it may be that not all constraints must be met, rather that they should be met if possible with some degree of preference. To express this, a utility value is added to represent the importance of some temporal constraints. Various search procedures are looked at to try to maximise the total utility whilst still maintaining consistency. It is found that a gre...

OCR post-processing is an important step to improve text accuracy. It includes two main tasks, error detection and correction. Hill climbing algorithm a heuristic search method used for solving optimization problems. In this paper, we present novel correction approach using adapted version of the algorithm. Correction candidates errors are explored by random character edits evolved with climbin...