The combination of the broad problem searching capabilities of a genetic algorithm with the local maxima location capabilities of a hill climbing algorithm can be a powerful technique for solving classification problems. Producing a number of specialist artificial neural networks, each an expert on one category, can be beneficial when solving problems in which the categories are distinct. This ...

This paper presents an efficient method for automatic training of performant visual object detectors, and its successful application to training of a back-view car detector. Our method for training detectors is adaBoost applied to a very general family of visual features (called “control-point” features), with a specific feature-selection weak-learner: evo-HC, which is a hybrid of Hill-Climbing...

An operational economic model for radio resource allocation in the downlink of a multi-cell WCDMA1 system is developed in this paper, and a particle swarm optimization (PSO) based approach is proposed for its solution. Firstly, we develop an economic model for resource allocation that considers the utility of the provided service, the acceptance probability of the service by the users and the r...

Recently, a hybrid methodology for combining genetic algorithms and local search algorithms has received considerable attention. This paper proposes an extended hybrid genetic algorithm to further improve the performance of finding the optimal solution in a large search space. Three key ideas, i.e. the elitism, nonredundant search, and steepest-ascent hill climbing, are introduced into a standa...

The university course timetabling problem deals with the assignment of lectures to specific timeslots and rooms. The goal is to satisfy the soft constraints to the largest degree possible while constructing a feasible schedule. In this paper, we present a hybrid approach consisting of three phases. During phase 1, initial solutions are generated using a constructive heuristic. An improvement ap...

The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the manifests that only implementation of MMHC in statistical software R is prohibitively expensive, and offered. specifically for case data, robust outliers version FEDHC, which can be adopted by othe...

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...