A toy optimisation problem is introduced which consists of a ÿtness gradient broken up by a series of hurdles. The performance of a hill-climber and a stochastic hill-climber are computed. These are compared with the empirically observed performance of a genetic algorithm (GA) with and without. The hill-climber with a suuciently large neighbourhood outperforms the stochastic hill-climber, but i...

Many learning systems search through a space of possible performance elements, seeking an element whose expected utility, over the distribution of problems, is high. As the task of nding the globally optimal element is often intractable, many practical learning systems instead hill-climb to a local optimum. Unfortunately, even this is problematic as the learner typically does not know the under...

A general hill-climbing attack algorithm based on Bayesian adaption is presented. The approach uses the scores provided by the matcher to adapt a global distribution computed from a development set of users, to the local specificities of the client being attacked. The proposed attack is evaluated on a competitive feature-based signature verification system over the 330 users of the MCYT databas...

In this paper, ANN controller for maximum power point tracking of photovoltaic (PV) systems is proposed and PV modeling is discussed. Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point. ANN controller with hillclimbing algorithm offers fast and accurate converging to the maximum operating point during stead...

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...

Evolutionary algorithms and hill-climbing search models are investigated to address the protein structure prediction problem. This is a well-known NP-hard problem representing one of the most important and challenging problems in computational biology. The pull move operation is engaged as the main local search operator in several approaches to protein structure prediction. The considered appro...

Hill climbing algorithms can train neural control systems for adaptive agents. They are an alternative to gradient descent algorithms especially if neural networks with non-layered topology or non-differentiable activation function are used, or if the task is not suitable for backpropagation training. This paper describes three variants of generic hill climbing algorithms which together can tra...

The ability to track dynamic functional optima is important in many practical tasks. Recent research in this area has concentrated on modifying evolutionary algorithms (EAs) by triggering changes in control parameters, ensuring population diversity, or remembering past solutions. A set of results are presented that favourably compare hill climbing with a genetic algorithm, and reasons for the r...

Genetic Algorithms are biologically inspired optimization algorithms. Performance of genetic algorithms mainly depends on type of genetic operators – Selection, Crossover, Mutation and Replacement used in it. Crossover operators are used to bring diversity in the population. This paper studies different crossover operators and then proposes a hybrid crossover operator that incorporates knowledg...