In this paper, we propose an integrated Genetic Algorithm with Hill Climbing to solve the matrix bandwidth minimization problem, which is to reduce bandwidth by permuting rows and columns resulting in the nonzero elements residing in a band as close as possible to the diagonal. Many algorithms for this problem have been developed, including the wellknown CM and GPS algorithms. Recently, Marti e...

In this paper we present a new iterative method to solve the maximum satisfiability problem (MAX SAT). This one aims to find the best assignment for a set of Boolean variables that gives the maximum of verified clauses in a Boolean formula. Unfortunately, It is shown that the MAX SAT problem is NP complete if the number of variable per clause is higher than 3. Our approach called QHILLSAT is a ...

We discuss the relationships between three approaches to greedy heuristic search: best-first, hill-climbing, and beam search. We consider the design decisions within each family and point out their oft-overlooked similarities. We consider the following best-first searches: weighted A*, greedy search,

Even though hill climbing search (HCS) control is the simplest MPPT algorithm that does not require any prior knowledge of the system, it has the disadvantage of being slow in its response. This slowness in the response is due to the number of perturbations involved in climbing the hill and the settling time of the each perturbation. This paper proposes an improved HCS control, in which the nat...

We present a method for solving service allocation problems in which a set of services must be allocated to a set of agents so as to maximize a global utility. The method is completely distributed so it can scale to any number of services without degradation. We first formalize the service allocation problem and then present a simple hill-climbing, a global hillclimbing, and a bidding-protocol ...

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators w...

The restricted setting and uniformly prescribed hardware of the Sony Legged League of RoboCup provide an environment for testing algorithms on autonomous robots with a view towards possible applications in real world situations. In this study we show how two techniques Support Vector Machines and Hill Climbing can be applied to problems faced by the robots in this league. We use Support Vector ...

Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring search space possible graphs and moving towards graph that maximises given objective function. Some algorithms offer exact guarantee to return highest score, while others approximate in exchange for reduced computational complexity. This paper describes an BN learning algorithm, which we call Model A...

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.

Previous work investigating the performance of genetic algorithms (GAs) has attempted to develop a set of fitness landscapes, called “Royal Roads” functions, which should be ideally suited for search with GAs. Surprisingly, many studies have shown that genetic algorithms actually perform worse than random mutation hill-climbing on these landscapes, and several different explanations have been o...