In this paper we present a hybrid search algorithm for solving constraint satisfaction and optimization problems. This algorithm combines ideas of two basic approaches: complete and incomplete algorithms which also known as systematic search and local search algorithms. Different characteristics of systematic search and local search methods are complementary. Therefore we have tried to get the ...

Most algorithms currently used to find good compilation sequences in an iterative adaptive compiler, such as genetic algorithms and hill climbing, search in the space of sequences of fixed length. In this paper, we argue that restricting the search to fixed-length sequences limits the ability of search algorithms to find good sequences for some benchmarks. We propose a new local search algorith...

There are many algorithms for optimizing the search engine results, ranking takes place according to one or more parameters such as; Backward Links, Forward Links, Content, click through rate and etc. The quality and performance of these algorithms depend on the listed parameters. The ranking is one of the most important components of the search engine that represents the degree of the vitality...

We study a class of random 3-SAT instances having exactly one solution. The properties of this ensemble considerably differ from those of a random 3-SAT ensemble. It is numerically shown that the running time of several complete and stochastic local search algorithms monotonically increases as the clause density is decreased. Therefore, there is no easy-hard-easy pattern of hardness as for stan...

| NK{landscapes ooer the ability to assess the performance of evolutionary algorithms on problems with different degrees of epistasis. In this paper, we study the performance of six algorithms in NK{landscapes with low and high dimension while keeping the amount of epistatic interactions constant. The results show that compared to genetic local search algorithms, the performance of standard gen...

In this extended abstract we present an algorithm based on the Ant Colony Optimization (ACO) metaheuristic for the single machine total weighted tardiness problem, a well known NP–hard scheduling problem. Our ACO algorithm is currently among the best algorithms known for this problem type. In particular, we will discuss three elements that enable it to find very good solutions quickly. These ar...

Hybridization of genetic algorithms with local search approaches can enhance their performance in global optimization. Genetic algorithms, as most population based algorithms, require a considerable number of function evaluations. This may be an important drawback when the functions involved in the problem are computationally expensive as it occurs in most real world problems. Thus, in order to...

Stochastic local search (SLS) methods like evolutionary algorithms, ant colony optimisation or iterated local search receive an ever increasing attention for the solution of highly application relevant optimisation problems. Despite their noteworthy successes, several issues still hinder their even wider spread. One central issue is the configuration and parameterisation of SLS methods, which i...

Two ant algorithms solving a simplified version of a typical university course timetabling problem are presented – Ant Colony System and MAX -MIN Ant System. The algorithms are tested over a set of instances from three classes of the problem. Results are compared with recent results obtained with several metaheuristics using the same local search routine (or neighborhood definition), and a refe...

We briefly review previous attempts to generate near-optimal solutions of the Traveling Salesman Problem by applying Genetic Algorithms. Following the lines of Johnson [1990] we discuss some possibilities for speeding up classical Local Search algorithms by casting them into a genetic frame. In an experimental study two such approaches, viz. Genetic Local Search with 2-Opt neighbourhoods and Li...