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...

We discuss the relationships between three approaches to greedy heuristic search: best-first, hill-climbing, and beam search. We consider the design decisions within each family and point out their oft-overlooked similarities. We consider the following best-first searches: weighted A*, greedy search,

The evolutionary multiobjective optimization technique for analog circuit optimizer is presented in this paper. the technique uses a Parallel Genetic Algorithm(PGA) to identifies multiple “good” solutions from a multiobjective fitness landscape which are tuned using a local hill-climbing algorithm. The PGA is used to provide a nature niching mechanism that has considerable computational advanta...

no unique method has been so far specified for determining the number of neurons in hidden layers of multi-layer perceptron (mlp) neural networks used for prediction. the present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. the data used in the present research for prediction are consumption data of water...

This paper describes a method of a cryptanalyst for a knapsack cipher. The deciphering method is based on the application of a heuristic random search hill-climbing algorithm, together with a genetic algorithm. It is shown that such an algorithm, implemented in Matlab environment, could be used to break a knapsack cipher. Some other aspects of the problem are discussed, too.

Program Transformations are generally written in order to generate better programs. In transformations, we apply a number of simple transformation axioms to parts of a program source code to obtain a functionally equivalent program. The application of these axioms is treated as a search problem and we apply a meta–heuristic search algorithm such as hill climbing to guide the direction of the se...

In this paper, we discuss the integration of complex aggregates in the construction of logical decision trees. We review the use of complex aggregates in TILDE, which is based on an exhaustive search in the complex aggregate space. As opposed to such a combinatorial search, we introduce a hill-climbing approach to build complex aggregates incrementally.

This paper describes JAMES, a modern object-oriented Java framework for discrete optimization using local search algorithms that exploits the generality of such metaheuristics by clearly separating search implementation and application from problem specification. A wide range of generic local searches are provided, including (stochastic) hill climbing, tabu search, variable neighbourhood search...