Hyperheuristics are single candidate solution based and simple to maintain mechanisms used in optimization. At each iteration, as a higher level of abstraction, a hyperheuristic chooses and applies one of the heuristics to a candidate solution. In this study, the performance contribution of hill climbing operators along with the mutational heuristics are analyzed in depth in four different hype...

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...

Reinforcement learning based on direct search in policy space requires few assumptions about the environment. Hence it is applicable in certain situations where most traditional reinforcement learning algorithms based on dynamic programming are not, especially in partially observable, deterministic worlds. In realistic settings, however, reliable policy evaluations are complicated by numerous s...

We hypothesize that evolutionary algorithms can effectively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitne...

L EARNING automata [1] have attracted a considerable interest in the last three decades. They are adaptive decision making devices that operate in unknown stochastic environments and progressively improve their performance via a learning process. They have been initially used by psychologists and biologists to describe the human behavior from both psychological and biological viewpoints. Learni...

Rolling stock allocation is the process of assigning timetable schedules to physical train units. This is primarily done by connecting together schedules at their terminal locations (known as schedule associations). Platforming allocation is the process of assigning those associations to particular platforms. A simple last-in, first-legal-out algorithm is used for rolling stock allocation that ...

A general hill-climbing attack to biometric systems based on a modification of the downhill simplex algorithm is presented. The scores provided by the matcher are used in this approach to adapt iteratively an initial estimate of the attacked template to the specificities of the client being attacked. The proposed attack is evaluated on a competitive feature-based signature verification system o...

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 ...

This paper describes a system for the unsupervised learning of morphological suffixes and stems from word lists. The system is composed of a generative probability model and hill-climbing and directed search algorithms. By extracting and examining morphologically rich subsets of an input lexicon, the directed search identifies highly productive paradigms. The hill-climbing algorithm then furthe...

In this paper, we consider the use of artificial intelligence techniques to aid in discovery of winning strategies for the Commons Game (CG). The game represents a common scenario in which multiple parties share the use of a self-replenishing resource. The resource deteriorates quickly if used indiscriminately. If used responsibly, however, the resource thrives. We consider the scenario one pla...