This study empirically investigates variations of hill climbing algorithms for training artiicial neural networks on the 5-bit parity classiication task. The experiments compare the algorithms when they use diierent combinations of random number distributions, variations in the step size and changes of the neural net-works' initial weight distribution. A hill climbing algorithm which uses inlin...

This paper describes the meta-level control system of a program (Dominic) for parametric design of mechanical components by iterative redesign. We view parametric design as search, and thus Dominic is a hill climbing algorithm. However, from experience with Dominic we concluded that modeling engineering design as hill climbing has several limitations. Therefore, a need for meta-level control kn...

For this thesis, different algorithms were created to solve the problem of Jop Shop Scheduling with a number of constraints. More specifically, the setting of a (simplified) car workshop was used: the algorithms had to assign all tasks of one day to the mechanics, taking into account minimum and maximum finish times of tasks and mechanics, the use of bridges, qualifications and the delivery and...

Topology potential field is a novel model to describe interaction and association of network nodes, which has attracted plenty of attention in community detection, node importance evaluation and network hot topics detection. The local maximum potential point search is a critical step for this research. Hill-climbing is a traditional algorithm for local maximum point search, which may leave out ...

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This chapter investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework o...

This paper presents an Improved Memetic Algorithm (IMA) designed to compute near-optimal solutions for the antibandwidth problem. It incorporates two distinguishing features: an efficient heuristic to generate a good quality initial population and a local search operator based on a Stochastic Hill Climbing algorithm. The most suitable combination of parameter values for IMA is determined by emp...

In this paper, we derive a method to refine a Bayes network diagnostic model by exploiting constraints implied by expert decisions on test ordering. At each step, the expert executes an evidence gathering test, which suggests the test’s relative diagnostic value. We demonstrate that consistency with an expert’s test selection leads to non-convex constraints on the model parameters. We incorpora...

1 Introduction In contrast to stationary problems where the problem parameters are static, dynamic optimisation problems (DOPs) present a challenging research area to the community [1]. This is mainly because, changes can occur any time during the optimisation course. Hence, when such a change happens (for example a change in the fitness landscape), the previous local optimal solution is no lon...

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators w...

Configuration similarity is a special form of content-based image retrieval that considers relative object locations. It can be used as a standalone method, or to complement retrieval based on visual or semantic features. The corresponding queries ask for sets of objects that satisfy some spatio-temporal constraints, e.g., “find all triplets of objects ( 1, 2, 3), such that 1 is northeast of 2,...