Late Acceptance Hill Climbing (LAHC) has been shown to be an effective local search method for several types of optimization problems, such as on certain types of scheduling problems as well as the traveling salesman problem. We apply LAHC to a central problem in the liner shipping industry, the Liner Shipping Fleet Repositioning Problem (LSFRP). The LSFRP involves the movement of vessels betwe...

Spatial orientation behavior is universal among animals, but its neuronal basis is poorly understood. The main objective of the present study was to identify candidate patterns of neuronal connectivity (motifs) for two widely recognized classes of spatial orientation behaviors: hill climbing, in which the organism seeks the highest point in a spatial gradient, and goal seeking, in which the org...

The modification of message that meets the sufficient conditions for collision is found in the last step of differential attack proposed by Wang et all. (2005) on MD4 hash algorithm. Here we show how this attack phase, finding a collision starting from the list of sufficient conditions for the collision, can be implemented using a combination of two algorithms evolutionary algorithm and hill cl...

We introduce a new approach to GA (Genetic Algorithms) based problem solving. Earlier GAs did not contain local search (i.e. hill climbing) mechanisms, which led to optimization difficulties, especially in higher dimensions. To overcome such difficulties, we introduce a "bug-based" search strategy, and implement a system called BUGS2. The ideas behind this new approach are derived from biologic...

This paper presents an efficient method for automatic training of performant visual object detectors, and its successful application to training of a back-view car detector. Our method for training detectors is adaBoost applied to a very general family of visual features (called “control-point” features), with a specific feature-selection weak-learner: evo-HC, which is a hybrid of Hill-Climbing...

Several local search algorithms for propositional satis ability have been pro posed which can solve hard random problems beyond the range of conventional backtracking procedures In this paper we explore the impact of focusing search in these procedures on the unsatis ed variables that is those variables which appear in clauses which are not yet satis ed For random problems we show that such a f...

M. Buehler, R. Battaglia, A. Cocosco, G. Hawker, J. Sarkis, K. Yamazaki. Centre for Intelligent Machines, McGill University, Montr eal, QC H3A 2A7, Canada Abstract A simple mechanical design for quadrupedal locomotion, termed SCOUT, is proposed, featuring only one degree of freedom per leg. This paper demonstrates experimentally that our rst prototype SCOUT-1 is capable of walking, turning, and...

Nowadays, many real problems can be solved using local search strategies. These algorithms incrementally alter inconsistency value assignments to all the variables using a repair or hill climbing metaphor to move towards more and more complete solutions. Furthermore, if the problem can be modeled as a distributed problem, the advantages can be even greater. This paper presents a distributed mod...

Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabilistic estimates. Searching a space using an evaluation function that is a random variable requires trading o accuracy of estimates for increased state explorat...

Usually the offspring-parent fitness correlation is used to visualize and analyze some caracteristics of fitness landscapes such as evolvability. In this paper, we introduce a more general representation of this correlation , the Fitness Cloud (FC). We use the bottleneck metaphor to emphasise fitness levels in landscape that cause local search process to slow down. For a local search heuristic ...