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...

Examination timetabling is a well-studied combinatorial optimization problem. We present a new hybrid algorithm for examination timetabling, consisting of three phases: a constraint programming phase to develop an initial solution, a simulated annealing phase to improve the quality of solution, and a hill climbing phase for further improvement. The examination timetabling problem at the Univers...

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper 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 of ...

We use directed search techniques in the space of computer programs to learn recursive sequences of positive integers. Specifically, the integer sequences of squares, x; cubes, x; factorial, x!; and Fibonacci numbers are studied. Given a small nite pre x of a sequence, we show that three directed searches|machine-language genetic programming with crossover, exhaustive iterative hill climbing, a...

We present a novel hybrid algorithm for Bayesian network structure learning, called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. It is based on a subroutine called HPC, that combines ideas from incremental and divide-andconquer constraint-based methods to learn the parents and ch...

The modification of message that meets the sufficient conditions for collision is found in the last step of differential attack proposed by Wang et all. (2005) on MD4 hash algorithm. Here we show how this attack phase, finding a collision starting from the list of sufficient conditions for the collision, can be implemented using a combination of two algorithms evolutionary algorithm and hill cl...

In this paper, we propose a hybrid approach for microarray image analysis, which is to quantify the intensity of each spot and locate differentially articulated genes with the aid of image processing and machine learning techniques. Initially we employ a hill-climbing automatic gridding and spot quantification technique, which takes a microarray image (or a sub-grid) as input, and makes no assu...

In this paper, we investigate a family of hill-climbing procedures related to GSAT, a greedy random hill-climbing procedure for satissability. These procedures are able to solve large and diicult satissability problems beyond the range of conventional procedures like Davis-Putnam. We explore the r^ ole of greediness, randomness and hill-climbing in the eeectiveness of these procedures. We show ...