The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computation effort. On the contrary, where this is not true, heuristics planners compute loads of useless node evaluations that make them scale-up poorly. In this paper we present a novel approach for boosting the scalability ...

A formal, representation-independent form of a memetic algorithm— a genetic algorithm incorporating local search—is introduced. A generalised form ofN -point crossover is defined together with representation-independent patching and hill-climbing operators. The resulting formal algorithm is then constructed and tested empirically on the travelling sales-rep problem. Whereas the genetic algorith...

We consider the algorithmic problem of selecting a set of target nodes that cause the biggest activation cascade in a network. In case when the activation process obeys the diminishing return property, a simple hill-climbing selection mechanism has been shown to achieve a provably good performance. Here we study models of influence propagation that exhibit critical behavior and where the proper...

Enforced hill climbing (EHC), a heuristicaa search method, has been frequently used in a number of AI planning systems. This paper presents a new form of EHC, guided enforced hill climbing (GEHC), to enhance EHC efficiency. Main feature in GEHC is an adaptive ordering function. GEHC has shown a significant improvement in EHC efficiency, especially when applied to larger problems.

In this paper, we aim at evaluating the impact of the starting point of a basic local search based on the first improvement strategy. We define the coverage rate of a configuration as the proportion of the search space from which a particular configuration can be reached by a strict hill-climbling with a non-zero probability. In particular, we compute the coverage rate of fitness landscapes glo...

In recent years, many researchers in AI and Robotics pay attention to RoboCup, because robotic soccer games needs various techniques in AI and Robotics, such as navigation, behavior generation, localization and environment recognition. Localization is one of the important issues for RoboCup. In this paper, we propose a method o f r obot's localization by integrating vision and modeling of the e...

This paper describes JAMES, a modern object-oriented Java framework for discrete optimization using local search algorithms that exploits the generality of such metaheuristics by clearly separating search implementation and application from problem specification. A wide range of generic local searches are provided, including (stochastic) hill climbing, tabu search, variable neighbourhood search...

This paper introduces a hybrid evolutionary hillclimbing algorithm that quickly solves (!onstraint, Satisfaction Problems (CSPs). This hybrid uses opportunistic arc and path revision in an interleaved fashion to reduce the size of the search space and to realize when to quit if a CSP is based on an inconsistent, constraint network. This hybrid out,performs a well known hill-climbing algorithm, ...

We investigate the use of oblivious, read-once decision graphs as structures for representing concepts over discrete domains, and present a bottom-up, hill-climbing algorithm for inferring these structures from labelled instances. The algorithm is robust with respect to irrelevant attributes , and experimental results show that it performs well on problems considered diicult for symbolic induct...

Subset selection problems are relevant in many domains. Unfortunately, their combinatorial nature prohibits solving them optimally in most cases. Local search algorithms have been applied to subset selection with varying degrees of success. This work presents COMPSET, a general algorithm for subset selection that invokes an existing local search algorithm from a random subset and its complement...