The use of macro-actions in planning introduces a trade-off. Macro-actions can offer search guidance by suggesting sequences of actions; but can potentially make search more expensive by increasing the branching factor. In this paper we present a technique for simulating the use of macro-actions by altering the order in which actions are considered for application during enforced hill-climbing ...

This paper reports a Fast Local Search (FLS) algorithm which helps to improve the efficiency of hill climbing and a Guided Local Search (GLS) Algorithm which is developed to help local search to escape local optima and distribute search effort. To illustrate how these algorithms work, this paper describes their application to British Telecom’s workforce scheduling problem, which is a hard real ...

Most local search algorithms are “perturbative”, incrementally moving from a search state to a neighbouring state while performing noisy hill-climbing. An alternative form of local search is “constructive”, repeatedly building partial solutions using greedy or other heuristics. Both forms have been combined with constraint propagation, and they can be hybridised with each other by perturbing pa...

In recent years, base sequences have been increasingly unscrambled through attempts represented by the human genome project. Accordingly, the estimation of the genetic network has been accelerated. However, no definitive method has become available for drawing a large effective graph. This paper proposes a method which allows for coping with an increase in the number of nodes by laying out gene...

We present a new heuristic method to evaluate planning states, which is based on solving a relaxation of the planning problem. The solutions to the relaxed problem give a good estimate for the length of a real solution, and they can also be used to guide action selection during planning. Using these informations, we employ a search strategy that combines Hill-climbing with systematic search. Th...

The paper deals with testing and evaluation of selected heuristic optimization methods Random Search, Downhill Simplex, Hill Climbing, Tabu Search, Local Search, Simulated Annealing, Evolution Strategy and Differential Evolution. We modified basic methods in such a way that they are applicable for discrete event simulation optimization purposes. The paper is mainly focused on testing Downhill S...

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...

The search is often used to for the shortest route, Hill Climbing Method a part of test that uses heuristic functions. problem encountered in form miscalculations calculating distance so it requires long distances, costs lot and takes very time. To solve this case, can be solved by making structure graph looking at city points from two sides point passed. Using an algorithm help make easier fin...

A class of problems referred to as Order Independent Minimum Grouping Problems is examined and an intuitive hill-climbing method for solving such problems is proposed. Example applications of this generic method are made to two well-known problems belonging to this class: graph colouring and bin packing. Using a wide variety of different problem instance-types, these algorithms are compared to ...