In this paper we consider the two-dimensional rectangular packing problem, where a fixed set of items have to be allocated on a single object. Two heuristics, which belong to the class of packing procedures that preserve bottom-left stability, are hybridised with three meta-heuristic algorithms (genetic algorithms, simulated annealing, naïve evolution) and local search heuristic (hill-climbing)...

We investigate the automatic construction of visual scenes via a hybrid evolutionary/hill-climbing approach using a correlationbased fitness function. This forms part of The Painting Fool system, an automated artist which is able to render the scenes using simulated art materials. We further describe a novel method for inventing fitness functions using the HR descriptive machine learning system...

Biometric recognition systems are vulnerable to numerous security threats. These include direct attacks to the sensor or indirect attacks, which represent the ones aimed towards internal system modules. In this work, indirect attacks against fingerprint verification systems are analyzed in order to better understand how harmful they can be. Software attacks via hill climbing algorithms are impl...

This paper presents a hybrid of genetic algorithm (GA) and local search operators for solving the three-matching problem (3MP). Several general / heuristic crossover and local hill-climbing operators are introduced. The adaptive process based on the observation of on-line performance of GA is used to select the feasible operators. In particular, the probability of applying an operator is increa...

This paper discusses the trade-off between accuracy, reliability and computing time in global optimization. Particular compromises provided by traditional methods (Quasi-Newton and Nelder-Mead's simplex methods) and genetic algorithms are addressed and illustrated by a particular application in the field of nonlinear system identification. Subsequently, new hybrid methods are designed, combinin...

This paper describes an approach for image segmentation that relies on learning from experience to adapt and improve the segmentation performance. The adaptive image segmentation system incorporates a feedback loop consisting of a machine learning subsystem, an image segmentation algorithm, and an evaluation component which determines segmentation quality. The machine learning component is base...

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...

MOTIVATION Bayesian network methods have shown promise in gene regulatory network reconstruction because of their capability of capturing causal relationships between genes and handling data with noises found in biological experiments. The problem of learning network structures, however, is NP hard. Consequently, heuristic methods such as hill climbing are used for structure learning. For netwo...

The method of nearest-neighbor interchange effects local improvements in a binary tree by replacing a 4-subtree by one of its two alternatives if this improves the objective function. We extend this to k-subtrees to reduce the number of local optima. Possible sequences of k-subtrees to be examined are produced by moving a window over the tree, incorporating one edge at a time while deactivating...

Evolutionary algorithms are sophisticated hill-climbers. In this paper, we discuss the ability of this class of local search algorithms to provide useful and eecient heuristics to solve NP-hard problems. Our discussion is illustrated on experiments aiming at solving the job-shop-scheduling problem. We focus on the components of the EA, pointing out the importance of the objective function as we...