This paper deenes a class of spaces which are easy for genetic algorithms and hard for stochastic hill{climbers. These spaces require genetic recombination for successful search and are partially deceptive. Problems where tradeoos need to be made subsume spaces with these properties. Preliminary results comparing a genetic algorithm without crossover against one with two{point crossover support...

This paper proposes a new method to solve certain classes of systems of multivariate equations over the binary field and its cryptanalytical applications. We show how heuristic optimization methods such as hill climbing algorithms can be relevant to solving systems of multivariate equations. A characteristic of equation systems that may be efficiently solvable by the means of such algorithms is...

Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabilistic estimates. Searching a space using an evaluation function that is a random variable requires trading off accuracy of estimates for increased state explor...

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 ...

A class of hybrid niching evolutionary algorithms (HNE) using clustering crowding and parallel local searching is proposed. By analyzing topology of fitness landscape and extending the space for searching similar individual, HNE determines the locality of search space more accurately, and decreases the replacement errors of crowding and suppressing genetic drift of the population. The integrati...

Hyper-heuristics are a class of high-level search methods used to solve computationally difficult problems, which operate on a search space of low-level heuristics rather than solutions directly. Previous work has shown that selection hyper-heuristics are able to solve many combinatorial optimisation problems, including the multidimensional 0-1 knapsack problem (MKP). The traditional framework ...

We investigate the potential of exhaustively exploring larger neighborhoods in local search algorithms for MINIMUM VERTEX COVER. More precisely, we study whether, for moderate values of k, it is feasible and worthwhile to determine, given a graph G with vertex cover C, if there is a k-swap S such that (C \S)∪ (S \C) is a smaller vertex cover of G. First, we describe an algorithm running in ∆ · ...

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 ...

IGUANA is a tool for automatically generating software test data using search-based approaches. Search-based approaches explore the input domain of a program for test data and are guided by a fitness function. The fitness function evaluates input data and measures how suitable it is for a given purpose, for example the execution of a particular statement in a program, or the falsification of an...