Heuristic Search techniques are known for their efficiency and effectiveness in solving NP-Hard problems. However, there has been limited success so far in constructing a software toolkit which is dedicated to these methods and can fully support all the stages and aspects of researching and developing a system based on these techniques. Some of the reasons for that include the lack of problem m...

Heuristic depressions are local minima of heuristic functions. While visiting one them, real-time (RT) search algorithms like LRTA∗ will update the heuristic value for most of their states several times before escaping, resulting in costly solutions. Existing RT search algorithm tackle this problem by doing more search and/or lookahead but do not guide search towards leaving depressions. We pre...

Meta-heuristics are methods that sit on top of local search algorithms. They perform the function of avoiding or escaping a local optimum and/or premature convergence. The aim of this paper is to survey, compare and contrast meta-heuristics for local search. First, we present the technique of local search (or hill climbing as it is sometimes known). We then present a table displaying the attrib...

Search-based optimization methods have been used for software engineering activities such as software testing. In the field of software testing, search-based test data generation refers to application of meta-heuristic optimization methods to generate test data that cover the code space of a program. Automatic test data generation that can cover all the paths of software is known as a major cha...

A significant body of literature has explored various research directions in hyper-heuristics (which can be thought as heuristics to choose heuristics). In this paper we define a graph based hyper-heuristic (GHH) framework and compare the performance of a number of local search based searching algorithms as the high level heuristics. They are used to search upon permutations of low level graph ...

In this paper, we develop new heuristic procedures for the maximum diversity problem (MDP). This NP-hard problem has a significant number of practical applications such as environmental balance, telecommunication services or genetic engineering. The proposed algorithm is based on the tabu search methodology and incorporates memory structures for both construction and improvement. Although propo...

In constraint satisfaction, local search is an incomplete method for finding a solution to a problem. Solving a general constraint satisfaction problem (CSP) is known to be NP-complete; so that heuristic techniques are usually used. The main contribution of this work is twofold: (i) a technique for de-composing a CSP into a DFS-tree CSP structure; (ii) an heuristic search technique for solving ...

In this chapter we present a hybrid evolutionary meta-heuristic based on memetic algorithms (MAs) and several local search algorithms. The memetic algorithm is used as the principal heuristic that guides the search and could use any of 16 local search algorithms during the search process. The local search algorithms used in combination with the MA are obtained by fixing either the type of the n...

This paper presents Inc*, a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall performance of the heuristic. Genetic programming is used to discover new strategies for the Inc* algorithm. We experimentally compare performance of local heuristics for SAT with and without the Inc* algorithm. Results sho...

response surface methodology is a common tool in optimizing processes. it mainly concerns situations when there is only one response of interest. however, many designed experiments often involve simultaneous optimization of several quality characteristics. this is called a multiresponse surface optimization problem. a common approach in dealing with these problems is to apply desirability funct...