First-order learning systems (e.g., FOlL, FOCL, FORTE) generally rely on hill-climbing heuristics in order to avoid the combinatorial explosion inherent in learning first-order concepts. However, hill-climbing leaves these systems vulnerable to local maxima and local plateaus. We present a method, called relational pathfinding, which has proven highly effective in escaping local maxima and cros...

First-order learning systems (e.g., FOIL, FOCL, FORTE) generally rely on hill-climbing heuristics in order to avoid the combinatorial explosion inherent in learning first-order concepts. However, hill-climbing leaves these systems vulnerable to local maxima and local plateaus. We present a method, called relational pathfinding, which has proven highly effective in escaping local maxima and cros...

The basic idea of local search is to find a good local optima quickly enough to approximate the global optima. This idea is somewhat like hill climbing. Starting from an arbitrary point, we try to go down or climb up a little step and compare the objective value at the new point to that at the old point. We then choose the step that increase the objective value. In this manner, we finally reach...

Abstract This chapter is devoted to random and memory-free methods that repeatedly construct solutions or modify them. Among the most popular techniques, there are simulated annealing, threshold accepting, great deluge demon algorithms, noising methods, late acceptance hill climbing, variable neighborhood search, GRASP.

Slightly expanded version of a paper submitted to AISB-95. Abstract Several local search algorithms for propositional satissability have recently been proposed which are able to 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 \unsatissed variables"; that is, those variables wi...

This paper presents a convergence analysis for the problem of consistent labelling using genetic search. The work builds on a recent empirical study of graph matching where we showed that a Bayesian consistency measure could be e$ciently optimised using a hybrid genetic search procedure which incorporated a hill-climbing step. In the present study we return to the algorithm and provide some the...

We provide preliminary details and formulation of an optimization strategy under current development that is able to automatically tune the parameters of a Support Vector Machine over new datasets. The optimization strategy is a heuristic based on Iterated Local Search, a modification of classic hill climbing which iterates calls to a local search routine.

We provide preliminary details and formulation of an optimization strategy under current development that is able to automatically tune the parameters of a Support Vector Machine over new datasets. The optimization strategy is a heuristic based on Iterated Local Search, a modification of classic hill climbing which iterates calls to a local search routine.

We provide an improved algorithm called “a neighborhood expansion tabu search algorithm based on genetic factors” (NETS) to solve traveling salesman problem. The algorithm keeps the traditional tabu algorithm’s neighborhood, ensure the algorithm’s strong climbing ability and go to the local optimization. At the same time, introduce the genetic algorithm’s genetic factor (crossover factor and va...

This paper examines the performance of hill climbing algo rithms on standard test problems for combinatorial auctions CAs On single unit CAs deterministic hill climbers are found to perform well and their performance can be improved signi cantly by randomizing them and restarting them several times or by using them collectively For some problems this good performance is shown to be no better th...